#reading spss file
import pyreadstat as prs
#data manipulation
import numpy as np
import pandas as pd
#data visualization
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
pio.renderers.default = "notebook"
#data preprocessing
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
#goefileloade/read
import geopandas as gpd
import json
#pandas settings
pd.set_option('display.max_columns', None)
pd.set_option('future.no_silent_downcasting', True)
#getting the data from the sav file
df, meta = prs.read_sav(r"C:\Users\ULTRAPC\Desktop\Final project\Project_Dataset.sav")
df
| version | doi | A_YEAR | B_COUNTRY | B_COUNTRY_ALPHA | C_COW_NUM | C_COW_ALPHA | D_INTERVIEW | J_INTDATE | FW_START | FW_END | K_TIME_START | K_TIME_END | K_DURATION | Q_MODE | N_REGION_ISO | N_REGION_WVS | N_TOWN | G_TOWNSIZE | G_TOWNSIZE2 | H_SETTLEMENT | H_URBRURAL | I_PSU | O1_LONGITUDE | O2_LATITUDE | S_INTLANGUAGE | LNGE_ISO | E_RESPINT | F_INTPRIVACY | W_WEIGHT | S018 | PWGHT | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Q12 | Q13 | Q14 | Q15 | Q16 | Q17 | Q18 | Q19 | Q20 | Q21 | Q22 | Q23 | Q24 | Q25 | Q26 | Q27 | Q28 | Q29 | Q30 | Q31 | Q32 | Q33 | Q33_3 | Q34 | Q34_3 | Q35 | Q35_3 | Q37 | Q38 | Q39 | Q40 | Q41 | Q42 | Q43 | Q44 | Q45 | Q46 | Q47 | Q48 | Q49 | Q50 | Q51 | Q52 | Q53 | Q54 | Q55 | Q56 | Q57 | Q58 | Q59 | Q60 | Q61 | Q62 | Q63 | Q64 | Q65 | Q66 | Q67 | Q68 | Q69 | Q70 | Q71 | Q72 | Q73 | Q74 | Q75 | Q76 | Q77 | Q78 | Q79 | Q80 | Q81 | Q82 | Q82_ARABLEAGUE | Q82_GULFCOOP | Q82_ISLCOOP | Q83 | Q84 | Q85 | Q86 | Q87 | Q88 | Q89 | Q90 | Q91 | Q92 | Q93 | Q94 | Q95 | Q96 | Q97 | Q98 | Q99 | Q100 | Q101 | Q102 | Q103 | Q104 | Q105 | Q106 | Q107 | Q108 | Q109 | Q110 | Q111 | Q112 | Q113 | Q114 | Q115 | Q116 | Q117 | Q118 | Q119 | Q120 | Q121 | Q122 | Q123 | Q124 | Q125 | Q126 | Q127 | Q128 | Q129 | Q130 | Q131 | Q132 | Q133 | Q134 | Q135 | Q136 | Q137 | Q138 | Q139 | Q140 | Q141 | Q142 | Q143 | Q144 | Q145 | Q146 | Q147 | Q148 | Q149 | Q150 | Q151 | Q152 | Q153 | Q154 | Q155 | Q156 | Q157 | Q158 | Q159 | Q160 | Q161 | Q162 | Q163 | Q164 | Q165 | Q166 | Q167 | Q168 | Q169 | Q170 | Q171 | Q172 | Q173 | Q174 | Q175 | Q176 | Q177 | Q178 | Q179 | Q180 | Q181 | Q182 | Q183 | Q184 | Q185 | Q186 | Q187 | Q188 | Q189 | Q190 | Q191 | Q192 | Q193 | Q194 | Q195 | Q196 | Q197 | Q198 | Q199 | Q200 | Q201 | Q202 | Q203 | Q204 | Q205 | Q206 | Q207 | Q208 | Q209 | Q210 | Q211 | Q212 | Q213 | Q214 | Q215 | Q216 | Q217 | Q218 | Q219 | Q220 | Q221 | Q222 | Q223 | Q223_ABREV | Q223_LOCAL | Q224 | Q225 | Q226 | Q227 | Q228 | Q229 | Q230 | Q231 | Q232 | Q233 | Q234 | Q234A | Q235 | Q236 | Q237 | Q238 | Q239 | Q240 | Q241 | Q242 | Q243 | Q244 | Q245 | Q246 | Q247 | Q248 | Q249 | Q250 | Q251 | Q252 | Q253 | Q254 | Q255 | Q256 | Q257 | Q258 | Q259 | Q260 | Q261 | Q262 | X003R | X003R2 | Q263 | Q264 | Q265 | Q266 | Q267 | Q268 | Q269 | Q270 | Q271 | Q272 | Q273 | Q274 | Q275 | Q275R | Q276 | Q276R | Q277 | Q277R | Q278 | Q278R | Q279 | Q280 | Q281 | Q282 | Q283 | Q284 | Q285 | Q286 | Q287 | Q288 | Q288R | Q289 | Q289CS9 | Q290 | Q291G1 | Q291G2 | Q291G3 | Q291G4 | Q291G5 | Q291G6 | Q291P1 | Q291P2 | Q291P3 | Q291P4 | Q291P5 | Q291P6 | Q291UN1 | Q291UN2 | Q291UN3 | Q291UN4 | Q291UN5 | Q291UN6 | Q292A | Q292B | Q292C | Q292D | Q292E | Q292F | Q292G | Q292H | Q292I | Q292J | Q292K | Q292L | Q292M | Q292N | Q292O | Q293 | Q294A | Q294B | Y001 | Y002 | Y003 | SACSECVAL | SACSECVALB | RESEMAVAL | RESEMAVALB | I_AUTHORITY | I_NATIONALISM | I_DEVOUT | DEFIANCE | I_RELIGIMP | I_RELIGBEL | I_RELIGPRAC | DISBELIEF | I_NORM1 | I_NORM2 | I_NORM3 | RELATIVISM | I_TRUSTARMY | I_TRUSTPOLICE | I_TRUSTCOURTS | SCEPTICISM | I_INDEP | I_IMAGIN | I_NONOBED | AUTONOMY | I_WOMJOB | I_WOMPOL | I_WOMEDU | EQUALITY | I_HOMOLIB | I_ABORTLIB | I_DIVORLIB | CHOICE | I_VOICE1 | I_VOICE2 | I_VOI2_00 | VOICE | SECVALWGT | WEIGHT1A | WEIGHT1B | WEIGHT2A | WEIGHT2B | WEIGHT3A | WEIGHT3B | WEIGHT4A | WEIGHT4B | RESEMAVALBWGT | RESEMAVALWGT | SECVALBWGT | Y001_1 | Y001_2 | Y001_3 | Y001_4 | Y001_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070001.0 | 20211112.0 | 202111.0 | 202112.0 | 10.05 | 10.43 | 38.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 2.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 2.0 | 1.0 | 3.0 | 2.0 | 3.0 | 5.0 | 8.0 | 6.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 2.0 | 1.0 | 3.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 1.0 | 4.0 | 4.0 | 4.0 | 3.0 | 2.0 | 1.0 | 1.0 | 3.0 | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 2.0 | 3.0 | 4.0 | 4.0 | 2.0 | 5.0 | 3.0 | 3.0 | 2.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 6.0 | 4.0 | 5.0 | 5.0 | 5.0 | 2.0 | 10.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 8.0 | 3.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 0.0 | 3.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 3.0 | 1.0 | 4.0 | 2.0 | 4.0 | 6.0 | 8.0 | 8.0 | 5.0 | 6.0 | 8.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 4.0 | 5.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 7.0 | 2.0 | 1.0 | 6.0 | 2.0 | 8.0 | 1.0 | 1.0 | 1.0 | 3.0 | 10.0 | 1.0 | 4.0 | 4.0 | 2.0 | 3.0 | 5.0 | 1.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 5.0 | 1.0 | 1.0 | 3.0 | 3.0 | 2.0 | 3.0 | 2.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 504011.0 | 504011.0 | 504011.0 | 1.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 3.0 | 2.0 | 5.0 | 4.0 | 4.0 | 4.0 | 2.0 | 1.0 | 8.0 | 10.0 | 7.0 | 6.0 | 9.0 | 4.0 | 8.0 | 9.0 | 5.0 | 10.0 | 10.0 | 3.0 | 1.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 4.0 | 2.0 | 1969.0 | 52.0 | 4.0 | 3.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 8.0 | 1.0 | 500.0 | 1.0 | 4.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 5.0 | 5.0 | 9.0 | 9.0 | 2.0 | 1.0 | 3.0 | 2.0 | 6.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 5.0 | 2.0 | 5.0 | 4.0 | 5.0 | 5.0 | 5.0 | 2.0 | 5.0 | 4.0 | 5.0 | 4.0 | 3.0 | 4.0 | 4.0 | 2.0 | 5.0 | 3.0 | 5.0 | 4.0 | 3.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 5.0 | 4.0 | 1.0 | 3.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | -2.0 | 0.331667 | 0.443333 | 0.235139 | 0.262778 | 1.0 | 0.66 | 0.00 | 0.553333 | 0.00 | 1.0 | 0.000000 | 0.333333 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.66 | 0.66 | 0.00 | 0.440000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.25 | 0.00 | 0.66 | 0.303333 | 0.000000 | 0.000000 | 0.666667 | 0.222222 | 0.33 | 0.5 | 0.415 | 0.415 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 | 1.0 | 0.0 |
| 1 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070002.0 | 20211112.0 | 202111.0 | 202112.0 | 10.46 | 11.19 | 33.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 2.0 | 3.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 1.0 | 1.0 | 4.0 | 4.0 | 5.0 | 4.0 | 2.0 | 4.0 | 1.0 | 1.0 | 2.0 | 4.0 | 3.0 | 2.0 | 1.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 1.0 | 1.0 | 4.0 | 4.0 | 3.0 | 1.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 1.0 | 3.0 | 3.0 | 3.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 5.0 | 3.0 | 2.0 | 2.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 10.0 | 10.0 | 9.0 | 6.0 | 6.0 | 1.0 | 10.0 | 3.0 | 4.0 | 3.0 | 1.0 | 4.0 | 4.0 | 0.0 | 10.0 | 1.0 | 2.0 | 2.0 | 0.0 | 0.0 | 2.0 | 2.0 | 2.0 | 2.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 4.0 | 1.0 | 3.0 | 2.0 | 3.0 | 10.0 | 10.0 | 10.0 | 10.0 | 8.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 5.0 | 5.0 | 1.0 | 2.0 | 2.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 8.0 | 5.0 | 5.0 | 5.0 | 5.0 | 9.0 | 5.0 | 5.0 | 5.0 | 5.0 | 6.0 | 1.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 5.0 | 1.0 | 5.0 | 1.0 | 1.0 | 4.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 504009.0 | 504009.0 | 504009.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1.0 | 3.0 | 2.0 | 1.0 | 5.0 | 1.0 | 1.0 | 4.0 | 1.0 | 1.0 | 1.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 5.0 | 8.0 | 6.0 | 7.0 | 3.0 | 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 2.0 | 1998.0 | 23.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 6.0 | 2.0 | 500.0 | 6.0 | 0.0 | 6.0 | 3.0 | 3.0 | 2.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | NaN | 4.0 | NaN | 6.0 | 3.0 | 2.0 | 2.0 | 4.0 | 7.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 5.0 | 1.0 | 5.0 | 5.0 | 5.0 | 5.0 | 1.0 | 1.0 | 1.0 | 5.0 | 5.0 | 5.0 | 1.0 | 5.0 | 3.0 | 3.0 | 3.0 | 2.0 | 5.0 | 5.0 | 1.0 | 1.0 | 5.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.0 | 1.0 | 1.0 | 0.0 | NaN | NaN | 2.0 | 1.0 | -1.0 | 0.360556 | 0.111111 | 0.360556 | 0.387778 | 0.0 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.0 | 0.666667 | 0.222222 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.00 | 0.00 | 0.66 | 0.220000 | 0.0 | 1.0 | 1.0 | 0.666667 | 0.00 | 0.00 | 0.66 | 0.220000 | 0.444444 | 0.444444 | 0.777778 | 0.555556 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 2 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070003.0 | 20211112.0 | 202111.0 | 202112.0 | 11.28 | 12.09 | 41.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 2.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 2.0 | 1.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 1.0 | 1.0 | 3.0 | 4.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 1.0 | 2.0 | 3.0 | 4.0 | 1.0 | 1.0 | 3.0 | 3.0 | 4.0 | 1.0 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 1.0 | 9.0 | 3.0 | 3.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 10.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 4.0 | 4.0 | 10.0 | 4.0 | 0.0 | 2.0 | 2.0 | 2.0 | 0.0 | 2.0 | 2.0 | 0.0 | 2.0 | 2.0 | 3.0 | 4.0 | 4.0 | 2.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 2.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 4.0 | 2.0 | 3.0 | 8.0 | 8.0 | 7.0 | 5.0 | 2.0 | 7.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 4.0 | 7.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 4.0 | 7.0 | 2.0 | 1.0 | 1.0 | 5.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 4.0 | 3.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 1.0 | 4.0 | 2.0 | 3.0 | 3.0 | 1.0 | 2.0 | 3.0 | 3.0 | 1.0 | 3.0 | 4.0 | 1.0 | 1.0 | 3.0 | 2.0 | 2.0 | 8.0 | 10.0 | 7.0 | 10.0 | 10.0 | 5.0 | 10.0 | 6.0 | 8.0 | 10.0 | 10.0 | 6.0 | 6.0 | 2.0 | 2.0 | 1.0 | 2.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1986.0 | 35.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 4.0 | 2.0 | 170.0 | 3.0 | 1.0 | 6.0 | 3.0 | 4.0 | 2.0 | 6.0 | 3.0 | 6.0 | 3.0 | 7.0 | NaN | 9.0 | 9.0 | 9.0 | 3.0 | 1.0 | 2.0 | 2.0 | 7.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 5.0 | 4.0 | 1.0 | 4.0 | 4.0 | 1.0 | 5.0 | 5.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 3.0 | 2.0 | 3.0 | 2.0 | 1.0 | 5.0 | 5.0 | 3.0 | 2.0 | 4.0 | 5.0 | 1.0 | 3.0 | 5.0 | 4.0 | 1.0 | 4.0 | 4.0 | 4.0 | 2.0 | 7.0 | NaN | NaN | 3.0 | 2.0 | 0.0 | 0.291389 | 0.249444 | 0.304583 | 0.360000 | 0.0 | 0.33 | 0.00 | 0.110000 | 0.00 | 1.0 | 0.166667 | 0.388889 | 1.0 | 1.0 | 0.0 | 0.666667 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.0 | 0.0 | 1.0 | 0.333333 | 0.50 | 0.00 | 0.66 | 0.386667 | 0.000000 | 0.333333 | 0.666667 | 0.333333 | 0.33 | 0.0 | 0.165 | 0.165 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 |
| 3 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070004.0 | 20211112.0 | 202111.0 | 202112.0 | 12.08 | 12.42 | 34.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 1.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 1.0 | 1.0 | 2.0 | 1.0 | 8.0 | 1.0 | 6.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 2.0 | 1.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 3.0 | 1.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 10.0 | 3.0 | 3.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 2.0 | 7.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 1.0 | 10.0 | 4.0 | 2.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 2.0 | 0.0 | 3.0 | 1.0 | 3.0 | 2.0 | 2.0 | 4.0 | 2.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 3.0 | 10.0 | 10.0 | 10.0 | 8.0 | 1.0 | 10.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 5.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 1.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 1.0 | 3.0 | 3.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 1.0 | 1.0 | 3.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | 4.0 | 4.0 | 4.0 | 3.0 | 5.0 | 10.0 | 10.0 | 10.0 | 10.0 | 1.0 | 10.0 | 10.0 | 1.0 | 1.0 | 10.0 | 6.0 | 6.0 | 1.0 | 1.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1993.0 | 28.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 4.0 | 2.0 | 170.0 | 1.0 | 2.0 | 1.0 | 1.0 | 0.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 7.0 | 1.0 | 3.0 | 0.0 | 9.0 | 2.0 | 2.0 | 2.0 | 3.0 | 6.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 2.0 | 1.0 | 1.0 | 2.0 | 5.0 | 3.0 | 4.0 | 1.0 | 1.0 | 2.0 | 5.0 | 4.0 | 1.0 | 1.0 | 1.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 5.0 | NaN | NaN | 1.0 | 1.0 | -1.0 | 0.000000 | 0.000000 | 0.055000 | 0.110000 | 0.0 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00 | 0.00 | 0.00 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00 | 0.00 | 0.66 | 0.220000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 4 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504070005.0 | 20211112.0 | 202111.0 | 202112.0 | 13.18 | 13.55 | 37.0 | 2.0 | 504009.0 | 504019.0 | 504099.0 | 1.0 | 1.0 | 4.0 | 2.0 | 99.0 | -8.84 | 29.49 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 2.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 3.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 3.0 | 2.0 | 4.0 | 4.0 | 3.0 | 1.0 | 3.0 | 2.0 | 2.0 | 3.0 | 2.0 | 7.0 | 7.0 | 4.0 | 2.0 | 3.0 | 2.0 | 4.0 | 4.0 | 3.0 | 2.0 | 1.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 4.0 | 4.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 5.0 | 2.0 | 1.0 | 3.0 | 1.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 6.0 | 5.0 | 3.0 | 1.0 | 5.0 | 1.0 | 8.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 10.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 0.0 | 2.0 | 0.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | 2.0 | 8.0 | 10.0 | 7.0 | 6.0 | 1.0 | 8.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 7.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 1.0 | 4.0 | 1.0 | 8.0 | 2.0 | 1.0 | 4.0 | 5.0 | 10.0 | 1.0 | 4.0 | 4.0 | 4.0 | 3.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 1.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 4.0 | 2.0 | 2.0 | 1.0 | 2.0 | 3.0 | 2.0 | 1.0 | 3.0 | 4.0 | 4.0 | 3.0 | 4.0 | 1.0 | 1.0 | 7.0 | 1.0 | 3.0 | 5.0 | 9.0 | 6.0 | 10.0 | 10.0 | 3.0 | 9.0 | 8.0 | 5.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 4.0 | 1.0 | 1999.0 | 22.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 5.0 | 1.0 | 170.0 | 6.0 | 0.0 | 1.0 | 1.0 | NaN | NaN | 3.0 | 2.0 | 2.0 | 1.0 | 1.0 | NaN | 5.0 | NaN | 9.0 | 3.0 | 2.0 | 3.0 | 4.0 | 7.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 2.0 | 4.0 | 1.0 | 1.0 | 4.0 | 5.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 2.0 | 4.0 | 4.0 | 1.0 | 4.0 | 4.0 | 4.0 | 5.0 | 5.0 | 2.0 | 2.0 | 4.0 | 5.0 | 1.0 | 1.0 | 5.0 | 5.0 | 1.0 | 5.0 | 5.0 | 4.0 | 5.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 0.317500 | 0.360000 | 0.276667 | 0.220000 | 0.5 | 0.66 | 0.00 | 0.386667 | 0.00 | 1.0 | 0.000000 | 0.333333 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.33 | 0.66 | 0.66 | 0.550000 | 1.0 | 0.0 | 1.0 | 0.666667 | 0.00 | 0.66 | 0.66 | 0.440000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
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| 1195 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071196.0 | 20211213.0 | 202111.0 | 202112.0 | 14.12 | 15.11 | 59.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 10.0 | 6.0 | 6.0 | 4.0 | 4.0 | 3.0 | 3.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 10.0 | 3.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 10.0 | 5.0 | 10.0 | 5.0 | 5.0 | 2.0 | 5.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 4.0 | 4.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 0.0 | 0.0 | 1.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 3.0 | 2.0 | 2.0 | 4.0 | 2.0 | 3.0 | 9.0 | 9.0 | 9.0 | 9.0 | 4.0 | 8.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 7.0 | 3.0 | 2.0 | 2.0 | 2.0 | 4.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 6.0 | 6.0 | 6.0 | 5.0 | 5.0 | 5.0 | 5.0 | 4.0 | 4.0 | 6.0 | 5.0 | 6.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 5.0 | 2.0 | 5.0 | 1.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 504004.0 | 504004.0 | 504004.0 | 1.0 | 4.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 3.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 1.0 | 3.0 | 10.0 | 7.0 | 5.0 | 7.0 | 6.0 | 6.0 | 7.0 | 6.0 | 7.0 | 6.0 | 10.0 | 10.0 | 10.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1980.0 | 41.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 5.0 | 1.0 | 170.0 | 6.0 | 0.0 | 4.0 | 2.0 | NaN | NaN | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | NaN | 1.0 | NaN | 6.0 | 2.0 | 1.0 | 2.0 | 3.0 | 6.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 2.0 | 4.0 | 4.0 | 1.0 | 4.0 | 2.0 | 2.0 | 5.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 5.0 | 4.0 | 4.0 | 5.0 | 3.0 | 2.0 | 0.526667 | 0.388333 | 0.628796 | 0.424259 | 0.0 | 0.33 | 0.00 | 0.110000 | 0.00 | 1.0 | 1.000000 | 0.666667 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.33 | 0.33 | 0.33 | 0.330000 | 1.0 | 0.0 | 1.0 | 0.666667 | 0.00 | 0.33 | 0.66 | 0.330000 | 0.444444 | 0.555556 | 0.555556 | 0.518519 | 1.00 | 1.0 | 1.000 | 1.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 1196 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071197.0 | 20211213.0 | 202111.0 | 202112.0 | 15.30 | 16.25 | 55.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 2.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 2.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 1.0 | 1.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 4.0 | 1.0 | 3.0 | 1.0 | 3.0 | 2.0 | 3.0 | 7.0 | 6.0 | 6.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 2.0 | 4.0 | 4.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 5.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 10.0 | 10.0 | 5.0 | 5.0 | 8.0 | 2.0 | 10.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 3.0 | 3.0 | 2.0 | 0.0 | 2.0 | 0.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 3.0 | 2.0 | 3.0 | 3.0 | 4.0 | 10.0 | 10.0 | 10.0 | 10.0 | 3.0 | 10.0 | 6.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 7.0 | 7.0 | 2.0 | 1.0 | 1.0 | 5.0 | 8.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 8.0 | 8.0 | 6.0 | 6.0 | 6.0 | 4.0 | 6.0 | 6.0 | 4.0 | 4.0 | 6.0 | 4.0 | 6.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 5.0 | 5.0 | 5.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 1.0 | 1.0 | 4.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.0 | 1.0 | 2.0 | 4.0 | 4.0 | 4.0 | 1.0 | 10.0 | 5.0 | 5.0 | 10.0 | 5.0 | 10.0 | 10.0 | 5.0 | 7.0 | 5.0 | 5.0 | 5.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1999.0 | 22.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 4.0 | 1.0 | 170.0 | 6.0 | 0.0 | 3.0 | 2.0 | NaN | NaN | 2.0 | 1.0 | 2.0 | 1.0 | 6.0 | NaN | 0.0 | NaN | 5.0 | 2.0 | 2.0 | 2.0 | 3.0 | 6.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 0.0 | NaN | NaN | 3.0 | 2.0 | 1.0 | 0.776667 | 0.610000 | 0.518889 | 0.581111 | 1.0 | 0.00 | 0.33 | 0.443333 | 0.33 | 1.0 | 1.000000 | 0.776667 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.66 | 1.00 | 1.00 | 0.886667 | 1.0 | 0.0 | 0.0 | 0.333333 | 0.50 | 0.66 | 0.66 | 0.606667 | 0.333333 | 0.777778 | 0.555556 | 0.555556 | 0.66 | 0.5 | 0.580 | 0.580 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 |
| 1197 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071198.0 | 20211213.0 | 202111.0 | 202112.0 | 16.02 | 17.10 | 68.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 4.0 | 2.0 | 4.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 3.0 | 4.0 | 3.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 1.0 | 3.0 | 3.0 | 1.0 | 2.0 | 3.0 | 6.0 | 10.0 | 3.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 3.0 | 1.0 | 1.0 | 3.0 | 2.0 | 4.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 4.0 | 8.0 | 5.0 | 5.0 | 9.0 | 1.0 | 7.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 9.0 | 1.0 | 2.0 | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 2.0 | 2.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 1.0 | 2.0 | 4.0 | 3.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 4.0 | 1.0 | 3.0 | 1.0 | 4.0 | 5.0 | 5.0 | 5.0 | 7.0 | 7.0 | 5.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 7.0 | 1.0 | 1.0 | 2.0 | 2.0 | 6.0 | 9.0 | 6.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 9.0 | 5.0 | 5.0 | 5.0 | 5.0 | 7.0 | 4.0 | 3.0 | 3.0 | 4.0 | 3.0 | 5.0 | 1.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 5.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 3.0 | 1.0 | 1.0 | 4.0 | 4.0 | 1.0 | 1.0 | 4.0 | 3.0 | 1.0 | 5.0 | 2.0 | 2.0 | 4.0 | 3.0 | 2.0 | 1.0 | 10.0 | 8.0 | 4.0 | 10.0 | 4.0 | 4.0 | 4.0 | 4.0 | 10.0 | 4.0 | 5.0 | 5.0 | 4.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1957.0 | 64.0 | 5.0 | 3.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 6.0 | 1.0 | 170.0 | 1.0 | 4.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 0.0 | 1.0 | 5.0 | 4.0 | 0.0 | 3.0 | 4.0 | 2.0 | 2.0 | 2.0 | 3.0 | 1.0 | 1.0 | 5.0 | 50000000.0 | 504005.0 | 4.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 4.0 | 2.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | NaN | NaN | 0.0 | 1.0 | -2.0 | 0.388333 | 0.166667 | 0.270278 | 0.540556 | 0.0 | 0.00 | 0.00 | 0.000000 | 0.00 | 0.0 | 1.000000 | 0.333333 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.00 | 0.33 | 0.33 | 0.220000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.25 | 0.66 | 1.00 | 0.636667 | 0.444444 | 0.444444 | 0.444444 | 0.444444 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1198 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071199.0 | 20211213.0 | 202111.0 | 202112.0 | 17.10 | 18.02 | 52.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 1.0 | 2.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 3.0 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 3.0 | 3.0 | 2.0 | 3.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 9.0 | 6.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 2.0 | 4.0 | 3.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 5.0 | 3.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 7.0 | 7.0 | 4.0 | 4.0 | 4.0 | 1.0 | 8.0 | 3.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 1.0 | 7.0 | 4.0 | 2.0 | 2.0 | 0.0 | 2.0 | 0.0 | 2.0 | 2.0 | 0.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 3.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 4.0 | 1.0 | 2.0 | 6.0 | 6.0 | 4.0 | 4.0 | 5.0 | 7.0 | 9.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 7.0 | 1.0 | 1.0 | 2.0 | 2.0 | 7.0 | 7.0 | 7.0 | 4.0 | 6.0 | 7.0 | 7.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 6.0 | 4.0 | 4.0 | 4.0 | 4.0 | 6.0 | 1.0 | 2.0 | 2.0 | 4.0 | 3.0 | 5.0 | 2.0 | 4.0 | 2.0 | 5.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 1.0 | 5.0 | 1.0 | 2.0 | 4.0 | 3.0 | 2.0 | 1.0 | 9.0 | 6.0 | 4.0 | 9.0 | 4.0 | 9.0 | 9.0 | 6.0 | 9.0 | 6.0 | 8.0 | 3.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1978.0 | 43.0 | 3.0 | 2.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 4.0 | 1.0 | 170.0 | 1.0 | 2.0 | 2.0 | 1.0 | 4.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5.0 | 2.0 | 0.0 | 3.0 | 6.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 1.0 | 5.0 | 50000000.0 | 504005.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 0.0 | NaN | NaN | 2.0 | 2.0 | -1.0 | 0.443333 | 0.221667 | 0.283194 | 0.483889 | 0.0 | 0.33 | 0.00 | 0.110000 | 0.00 | 0.0 | 1.000000 | 0.333333 | 1.0 | 1.0 | 1.0 | 1.000000 | 0.33 | 0.33 | 0.33 | 0.330000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.25 | 0.66 | 0.66 | 0.523333 | 0.666667 | 0.333333 | 0.333333 | 0.444444 | 0.33 | 0.0 | 0.165 | 0.165 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 |
| 1199 | 0.0 | 0.0 | 2021.0 | 504.0 | MAR | 600.0 | MOR | 504071200.0 | 20211213.0 | 202111.0 | 202112.0 | 17.40 | 18.13 | 34.0 | 2.0 | 504002.0 | 504012.0 | 504081.0 | 5.0 | 3.0 | 3.0 | 1.0 | 81.0 | -2.73 | 34.94 | 170.0 | ar | 1.0 | 1.0 | 1.0 | 0.833333 | 31083.33333 | 1.0 | 1.0 | 2.0 | 4.0 | 1.0 | 1.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 3.0 | 1.0 | 3.0 | 3.0 | 1.0 | 4.0 | 2.0 | 1.0 | 1.0 | 3.0 | 3.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 1.0 | 2.0 | 2.0 | 1.0 | 7.0 | 8.0 | 7.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 2.0 | 3.0 | 1.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 3.0 | 3.0 | 5.0 | 2.0 | 3.0 | 3.0 | 1.0 | 2.0 | 2.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 0.0 | 10.0 | 10.0 | 8.0 | 1.0 | 1.0 | 2.0 | 8.0 | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 4.0 | 3.0 | 2.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 3.0 | 2.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 3.0 | 1.0 | 2.0 | 1.0 | 2.0 | 3.0 | 4.0 | 1.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1.0 | 3.0 | 1.0 | 2.0 | 10.0 | 10.0 | 7.0 | 7.0 | 3.0 | 5.0 | 10.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 6.0 | 6.0 | 2.0 | 1.0 | 2.0 | 9.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 7.0 | 7.0 | 1.0 | 1.0 | 1.0 | 1.0 | 8.0 | 1.0 | 1.0 | 1.0 | 1.0 | 8.0 | 2.0 | 4.0 | 2.0 | 2.0 | 3.0 | 1.0 | 5.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | 2.0 | 2.0 | 2.0 | 1.0 | 3.0 | 3.0 | 3.0 | 1.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 4.0 | 4.0 | 3.0 | 4.0 | 1.0 | 1.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 3.0 | 1.0 | 4.0 | 3.0 | 2.0 | 4.0 | 2.0 | 1.0 | 7.0 | 8.0 | 9.0 | 10.0 | 8.0 | 1.0 | 9.0 | 7.0 | 8.0 | 9.0 | 9.0 | 7.0 | 5.0 | 2.0 | 2.0 | 1.0 | 2.0 | 2.0 | 3.0 | 3.0 | 1.0 | 1998.0 | 23.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 504.0 | 504.0 | 504.0 | 1.0 | 3.0 | 1.0 | 170.0 | 6.0 | 0.0 | 4.0 | 2.0 | NaN | NaN | 2.0 | 1.0 | 2.0 | 1.0 | 7.0 | NaN | 0.0 | NaN | 5.0 | 3.0 | 2.0 | 2.0 | 3.0 | 7.0 | 2.0 | 5.0 | 50000000.0 | 504005.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 5.0 | 2.0 | 2.0 | 4.0 | 5.0 | 2.0 | 2.0 | 5.0 | 2.0 | 2.0 | 4.0 | 2.0 | 5.0 | 5.0 | 3.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 1.0 | 1.0 | 1.0 | 0.331944 | 0.443889 | 0.311944 | 0.457222 | 0.5 | 0.33 | 0.00 | 0.276667 | 0.00 | 1.0 | 0.833333 | 0.611111 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00 | 0.66 | 0.66 | 0.440000 | 0.0 | 0.0 | 1.0 | 0.333333 | 0.75 | 0.00 | 0.66 | 0.470000 | 0.000000 | 0.666667 | 0.666667 | 0.444444 | 0.00 | 0.0 | 0.000 | 0.000 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
1200 rows × 430 columns
#setting up the variable view
variable_view = pd.DataFrame({'Column' : meta.column_names,
'Label': meta.column_labels ,
'Value' : [meta.variable_value_labels.get(var,{}) for var in meta.column_names]
})
variable_view
| Column | Label | Value | |
|---|---|---|---|
| 0 | version | Version of Data File | {} |
| 1 | doi | Digital Object Identifier | {} |
| 2 | A_YEAR | Year of survey | {2017.0: '2017', 2018.0: '2018', 2019.0: '2019... |
| 3 | B_COUNTRY | ISO 3166-1 numeric country code | {504.0: 'Morocco'} |
| 4 | B_COUNTRY_ALPHA | ISO 3166-1 alpha-3 country code | {} |
| ... | ... | ... | ... |
| 425 | Y001_1 | Materialist/postmaterialist 12-item index: Com... | {-2.0: '-2', 0.0: '0', 1.0: '1'} |
| 426 | Y001_2 | Materialist/postmaterialist 12-item index: Com... | {-2.0: '-2', 0.0: '0', 1.0: '1'} |
| 427 | Y001_3 | Materialist/postmaterialist 12-item index: Com... | {-2.0: '-2', 0.0: '0', 1.0: '1'} |
| 428 | Y001_4 | Materialist/postmaterialist 12-item index: Com... | {-2.0: '-2', 0.0: '0', 1.0: '1'} |
| 429 | Y001_5 | Materialist/postmaterialist 12-item index: Com... | {-2.0: '-2', 0.0: '0', 1.0: '1'} |
430 rows × 3 columns
#setting up the labeled_df
labeled_df = df.copy()
for column, value in meta.variable_value_labels.items():
if column in labeled_df.columns:
labeled_df[column] = labeled_df[column].map(value).fillna(labeled_df[column])
labeled_df
| version | doi | A_YEAR | B_COUNTRY | B_COUNTRY_ALPHA | C_COW_NUM | C_COW_ALPHA | D_INTERVIEW | J_INTDATE | FW_START | FW_END | K_TIME_START | K_TIME_END | K_DURATION | Q_MODE | N_REGION_ISO | N_REGION_WVS | N_TOWN | G_TOWNSIZE | G_TOWNSIZE2 | H_SETTLEMENT | H_URBRURAL | I_PSU | O1_LONGITUDE | O2_LATITUDE | S_INTLANGUAGE | LNGE_ISO | E_RESPINT | F_INTPRIVACY | W_WEIGHT | S018 | PWGHT | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Q12 | Q13 | Q14 | Q15 | Q16 | Q17 | Q18 | Q19 | Q20 | Q21 | Q22 | Q23 | Q24 | Q25 | Q26 | Q27 | Q28 | Q29 | Q30 | Q31 | Q32 | Q33 | Q33_3 | Q34 | Q34_3 | Q35 | Q35_3 | Q37 | Q38 | Q39 | Q40 | Q41 | Q42 | Q43 | Q44 | Q45 | Q46 | Q47 | Q48 | Q49 | Q50 | Q51 | Q52 | Q53 | Q54 | Q55 | Q56 | Q57 | Q58 | Q59 | Q60 | Q61 | Q62 | Q63 | Q64 | Q65 | Q66 | Q67 | Q68 | Q69 | Q70 | Q71 | Q72 | Q73 | Q74 | Q75 | Q76 | Q77 | Q78 | Q79 | Q80 | Q81 | Q82 | Q82_ARABLEAGUE | Q82_GULFCOOP | Q82_ISLCOOP | Q83 | Q84 | Q85 | Q86 | Q87 | Q88 | Q89 | Q90 | Q91 | Q92 | Q93 | Q94 | Q95 | Q96 | Q97 | Q98 | Q99 | Q100 | Q101 | Q102 | Q103 | Q104 | Q105 | Q106 | Q107 | Q108 | Q109 | Q110 | Q111 | Q112 | Q113 | Q114 | Q115 | Q116 | Q117 | Q118 | Q119 | Q120 | Q121 | Q122 | Q123 | Q124 | Q125 | Q126 | Q127 | Q128 | Q129 | Q130 | Q131 | Q132 | Q133 | Q134 | Q135 | Q136 | Q137 | Q138 | Q139 | Q140 | Q141 | Q142 | Q143 | Q144 | Q145 | Q146 | Q147 | Q148 | Q149 | Q150 | Q151 | Q152 | Q153 | Q154 | Q155 | Q156 | Q157 | Q158 | Q159 | Q160 | Q161 | Q162 | Q163 | Q164 | Q165 | Q166 | Q167 | Q168 | Q169 | Q170 | Q171 | Q172 | Q173 | Q174 | Q175 | Q176 | Q177 | Q178 | Q179 | Q180 | Q181 | Q182 | Q183 | Q184 | Q185 | Q186 | Q187 | Q188 | Q189 | Q190 | Q191 | Q192 | Q193 | Q194 | Q195 | Q196 | Q197 | Q198 | Q199 | Q200 | Q201 | Q202 | Q203 | Q204 | Q205 | Q206 | Q207 | Q208 | Q209 | Q210 | Q211 | Q212 | Q213 | Q214 | Q215 | Q216 | Q217 | Q218 | Q219 | Q220 | Q221 | Q222 | Q223 | Q223_ABREV | Q223_LOCAL | Q224 | Q225 | Q226 | Q227 | Q228 | Q229 | Q230 | Q231 | Q232 | Q233 | Q234 | Q234A | Q235 | Q236 | Q237 | Q238 | Q239 | Q240 | Q241 | Q242 | Q243 | Q244 | Q245 | Q246 | Q247 | Q248 | Q249 | Q250 | Q251 | Q252 | Q253 | Q254 | Q255 | Q256 | Q257 | Q258 | Q259 | Q260 | Q261 | Q262 | X003R | X003R2 | Q263 | Q264 | Q265 | Q266 | Q267 | Q268 | Q269 | Q270 | Q271 | Q272 | Q273 | Q274 | Q275 | Q275R | Q276 | Q276R | Q277 | Q277R | Q278 | Q278R | Q279 | Q280 | Q281 | Q282 | Q283 | Q284 | Q285 | Q286 | Q287 | Q288 | Q288R | Q289 | Q289CS9 | Q290 | Q291G1 | Q291G2 | Q291G3 | Q291G4 | Q291G5 | Q291G6 | Q291P1 | Q291P2 | Q291P3 | Q291P4 | Q291P5 | Q291P6 | Q291UN1 | Q291UN2 | Q291UN3 | Q291UN4 | Q291UN5 | Q291UN6 | Q292A | Q292B | Q292C | Q292D | Q292E | Q292F | Q292G | Q292H | Q292I | Q292J | Q292K | Q292L | Q292M | Q292N | Q292O | Q293 | Q294A | Q294B | Y001 | Y002 | Y003 | SACSECVAL | SACSECVALB | RESEMAVAL | RESEMAVALB | I_AUTHORITY | I_NATIONALISM | I_DEVOUT | DEFIANCE | I_RELIGIMP | I_RELIGBEL | I_RELIGPRAC | DISBELIEF | I_NORM1 | I_NORM2 | I_NORM3 | RELATIVISM | I_TRUSTARMY | I_TRUSTPOLICE | I_TRUSTCOURTS | SCEPTICISM | I_INDEP | I_IMAGIN | I_NONOBED | AUTONOMY | I_WOMJOB | I_WOMPOL | I_WOMEDU | EQUALITY | I_HOMOLIB | I_ABORTLIB | I_DIVORLIB | CHOICE | I_VOICE1 | I_VOICE2 | I_VOI2_00 | VOICE | SECVALWGT | WEIGHT1A | WEIGHT1B | WEIGHT2A | WEIGHT2B | WEIGHT3A | WEIGHT3B | WEIGHT4A | WEIGHT4B | RESEMAVALBWGT | RESEMAVALWGT | SECVALBWGT | Y001_1 | Y001_2 | Y001_3 | Y001_4 | Y001_5 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070001.0 | 20211112.0 | 202111.0 | 202112.0 | 10.05 | 10.43 | 38.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were other people around who could follo... | No weighting | 0.833333 | 31083.33333 | Very important | Rather important | Very important | Very important | Very important | Very important | Important | Not mentioned | Important | Important | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Important | Not mentioned | Important | Mentioned | Not mentioned | Not mentioned | Mentioned | Mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Agree strongly | Agree | Agree strongly | Disagree | Agree strongly | Agree strongly | Agree | Agree | Agree strongly | Agree | Strongly agree | Agree | Agree | Agree strongly | Strongly agree | Strongly agree | Neither agree nor disagree | Our society must be gradually improved by reforms | Don't mind | Good thing | Bad thing | Quite happy | Fair | 5 | 8 | 6 | Never | Rarely | Never | Never | Never | Or about the same | Need to be very careful | Trust completely | Do not trust very much | Trust somewhat | Do not trust at all | Do not trust at all | Trust somewhat | Quite a lot | Not very much | Not very much | Quite a lot | Not very much | Not very much | A great deal | None at all | None at all | None at all | Not very much | Quite a lot | A great deal | A great deal | Not very much | A great deal | Quite a lot | Not very much | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Not very much | Quite a lot | Not very much | None at all | None at all | Quite a lot | 5 | India | Geneva | Human rights | Inactive member | Don't belong | Don't belong | Not a member | Not a member | Don't belong | Not a member | Not a member | Don't belong | Don't belong | Don't belong | Don't belong | 6 | 4 | 5 | 5 | 5 | Economy growth and creating jobs | 10 There is abundant corruption in my country | Most of them | Most of them | Few of them | Few of them | Most of them | Frequently | Disagree | 8 | Neither good, nor bad | Agree | Agree | Hard to say | Agree | Hard to say | Agree | Hard to say | Disagree | Place strict limits on the number of foreigner... | Quite secure | Not frequently | Not frequently | Quite frequently | Quite frequently | Not frequently | Not frequently | Not frequently | Yes | No | Yes | A great deal | A great deal | No | No | Very much | Very much | A great deal | Equality | Security | No | A high level of economic growth | Seeing that people have more say about how ar... | Maintaining order in the nation | Protecting freedom of speech | Progress toward a less impersonal and more hum... | The fight against crime | 6 | 8 | 8 | 5 | 6 | 8 | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | More than once a week | Several times a day | Not a religious person | Do good to other people | Make sense of life after death | 4 | 5 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | 7 | 2 | Never justifiable | 6 | 2 | 8 | Never justifiable | Never justifiable | Never justifiable | 3 | Always justifiable | Definitely should have the right | Definitely should not have the right | Definitely should not have the right | Somewhat interested | Never | Never | Daily | Weekly | Weekly | Less than monthly | Weekly | Weekly | Never | Have done | Have done | Would never do | Would never do | Might do | Would never do | Might do | Would never do | Might do | Would never do | Would never do | Would never do | Usually | Usually | MAR: Authenticity and Modernity Party | MAR: PAM | MAR: Parti de l'Authenticite et de la Modernit... | Very often | Not often | Fairly often | Very often | Very often | Very often | Very often | Not at all often | Not at all often | Not often | Rather important | Not at all | Very bad | Very bad | Very bad | Fairly good | Very good | 8 | An essential characteristic of democracy | 7 | 6 | 9 | 4 | 8 | 9 | 5 | An essential characteristic of democracy | Absolutely important | 3 | Not satisfied at all | Not much respect | Not very proud | Not close at all | Not close at all | Not close at all | Not very close | Not close at all | Female | 1969.0 | 52.0 | 45-54 | 50 and more years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 8.0 | No | Berber; Amazigh;Tamaziɣt | Married | 4 children | Primary education (ISCED 1) | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Full time (30 hours a week or more) | Homemaker not otherwise employed | Service (for example: restaurant owner, police... | Farm worker (for example: farm labourer, tract... | Farm worker (for example: farm labourer, tract... | Private business or industry | Yes | Spent some savings | Upper middle class | Sixth step | Medium | Muslim | Islam; nfd | MA: Arabe | Disagree strongly | Agree | Disagree strongly | Disagree | Disagree strongly | Disagree strongly | Disagree strongly | Agree | Disagree strongly | Disagree | Disagree strongly | Disagree | Neither agree nor disagree | Disagree | Disagree | Agree | Disagree strongly | Neither agree nor disagree | Agree strongly | Agree | Neither agree nor disagree | Disagree | Disagree | Agree | Disagree | Disagree | Disagree strongly | Disagree strongly | Disagree strongly | Agree strongly | Agree | Disagree strongly | Neither agree nor disagree | 1 | 1 | 1 | 3 | Mixed | Obedience/Religious Faith | 0.331667 | 0.443333 | 0.235139 | 0.262778 | High | High | Very low | 0.553333 | Very low | Not religious or atheist | 0.0 | 0.333333 | Conformist | Conformist | Conformist | 0.0 | Low | Low | Very high | 0.44 | No | No | No | 0.0 | Medium-Low | Very low | High | 0.303333 | 0.0 | 0.0 | 0.666667 | 0.222222 | High | Medio | 0.415 | 0.415 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 1 | 0 | 1 | 1 | 0 |
| 1 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070002.0 | 20211112.0 | 202111.0 | 202112.0 | 10.46 | 11.19 | 33.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Rather important | Not very important | Not at all important | Very important | Very important | Important | Not mentioned | Important | Not mentioned | Important | Not mentioned | Important | Not mentioned | Important | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Mentioned | Mentioned | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Agree strongly | Disagree | Agree strongly | Agree strongly | Agree strongly | Agree | Agree strongly | Agree | Disagree strongly | Disagree | Agree strongly | Agree strongly | Strongly agree | Strongly agree | Agree strongly | Our present society must be valiantly defended... | Bad thing | Good thing | Good thing | Not at all happy | Poor | 5 | 4 | 2 | Never | Often | Often | Sometimes | Never | Or about the same | Need to be very careful | Trust completely | Do not trust very much | Do not trust at all | Do not trust at all | Do not trust at all | Do not trust very much | A great deal | A great deal | None at all | None at all | Not very much | A great deal | Not very much | None at all | None at all | None at all | Not very much | Not very much | None at all | None at all | None at all | Quite a lot | Quite a lot | A great deal | Not very much | Not very much | Not very much | Quite a lot | None at all | None at all | None at all | None at all | None at all | None at all | None at all | 5 | India | London | Human rights | Active member | Don't belong | Active member | Not a member | Not a member | Don't belong | Not a member | Not a member | Don't belong | Don't belong | Don't belong | Don't belong | There should be greater incentives for individ... | Government ownership of business should be inc... | 9 | 6 | 6 | Protecting environment | 10 There is abundant corruption in my country | Most of them | All of them | Most of them | None of them | All of them | Always | Hard to say | Very high risk | Rather bad | Agree | Agree | Disagree | Disagree | Agree | Agree | Agree | Agree | Prohibit people coming here from other countries | Quite secure | Very Frequently | Very Frequently | Very Frequently | Very Frequently | Quite frequently | Very Frequently | Very Frequently | Yes | Yes | No | Very much | Very much | Yes | Yes | Very much | Very much | Very much | Freedom | Security | Yes | A high level of economic growth | Trying to make our cities and countryside more... | Maintaining order in the nation | Fighting rising prices | Progress toward a less impersonal and more hum... | Progress toward a society in which Ideas count... | Completely agree | Completely agree | Completely agree | Completely agree | 8 | A lot worse off | Not at all important | Yes | No | No | No | Strongly disagree | Strongly disagree | Once a year | Only on special holy days | A religious person | Do good to other people | Make sense of life in this world | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 8 | 5 | 5 | 5 | 5 | 9 | 5 | 5 | 5 | 5 | 6 | Definitely should have the right | Definitely should not have the right | Definitely should not have the right | Somewhat interested | Occasionally | Less than monthly | Less than monthly | Never | Daily | Never | Daily | Daily | Less than monthly | Might do | Have done | Might do | Might do | Have done | Might do | Might do | Would never do | Might do | Might do | Would never do | Would never do | Never | Never | MAR: Justice and Development Party | MAR: PJD | MAR: Parti de la Justice et du Developpement -... | Not at all often | Not often | Not at all often | Not at all often | Not at all often | Not at all often | Very often | Very often | Not often | Fairly often | Very important | Not at all | Very good | Very good | Very bad | Very good | Very good | Left | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | 5 | 8 | 6 | 7 | Not much respect | Very proud | Very close | Very close | Close | Not very close | Not close at all | Female | 1998.0 | 23.0 | 16-24 | 16-29 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 6 | Yes, own parent(s) | Berber; Amazigh;Tamaziɣt | Single | No children | Bachelor or equivalent (ISCED 6) | Higher | Upper secondary education (ISCED 3) | Middle | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Full time (30 hours a week or more) | NaN | Sales (for example: sales manager, shop owner,... | NaN | Skilled worker (for example: foreman, motor me... | Private non-profit organization | No | Just get by | Working class | Seventh step | Medium | Muslim | Islam; nfd | MA: Arabe | Disagree strongly | Agree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Agree strongly | Agree strongly | Agree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Agree strongly | Disagree strongly | Neither agree nor disagree | Neither agree nor disagree | Neither agree nor disagree | Agree | Agree strongly | Agree strongly | Disagree strongly | Disagree strongly | Agree strongly | Agree | Disagree | Disagree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Disagree strongly | Agree strongly | Disagree strongly | Disagree strongly | No trust at all | NaN | NaN | 2 | Materialist | -1 | 0.360556 | 0.111111 | 0.360556 | 0.387778 | Low | Very low | Very low | 0.0 | Very low | Religious | 0.666667 | 0.222222 | Inconformist | Inconformist | Inconformist | 1.0 | Very high | Very high | Low | 0.22 | No | Yes | Yes | 0.666667 | Low | Very low | High | 0.22 | 0.444444 | 0.444444 | 0.777778 | 0.555556 | Very high | Bajo | 0.0 | 0.0 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 1 | 1 |
| 2 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070003.0 | 20211112.0 | 202111.0 | 202112.0 | 11.28 | 12.09 | 41.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were other people around who could follo... | No weighting | 0.833333 | 31083.33333 | Very important | Rather important | Very important | Not at all important | Very important | Very important | Important | Not mentioned | Important | Important | Not mentioned | Important | Not mentioned | Important | Important | Not mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Agree strongly | Disagree | Agree | Disagree | Neither agree nor disagree | Neither agree nor disagree | Agree strongly | Agree | Strongly agree | Agree | Agree strongly | Agree strongly | Strongly agree | Strongly agree | Agree strongly | The entire way our society is organized must b... | Bad thing | Good thing | Good thing | Not very happy | Poor | 2 | 2 | 3 | Rarely | Rarely | Sometimes | Often | Often | Worse off | Need to be very careful | Trust completely | Do not trust at all | Do not trust at all | Do not trust at all | Do not trust at all | Do not trust at all | Quite a lot | A great deal | Quite a lot | Not very much | None at all | A great deal | A great deal | Not very much | Not very much | None at all | A great deal | Quite a lot | None at all | A great deal | A great deal | A great deal | Not very much | Not very much | None at all | None at all | None at all | None at all | Not very much | None at all | None at all | None at all | A great deal | Quite a lot | A great deal | 9 | India | Geneva | Human rights | Don't belong | Active member | Don't belong | Not a member | Not a member | Don't belong | Not a member | Not a member | Don't belong | Don't belong | Don't belong | Don't belong | Incomes should be made more equal | Private ownership of business should be increased | The government should take more responsibility... | Competition is good | In the long run, hard work usually brings a be... | Protecting environment | 10 There is abundant corruption in my country | Few of them | Most of them | Few of them | Few of them | Most of them | Always | Strongly disagree | Very high risk | Quite good | Disagree | Agree | Agree | Agree | Disagree | Agree | Agree | Disagree | Let people come as long as there are jobs avai... | Quite secure | Not frequently | Not at all frequently | Not at all frequently | Quite frequently | Not frequently | Not frequently | Very Frequently | Yes | Yes | No | Not at all | A great deal | Yes | Yes | Very much | Very much | Very much | Equality | Freedom | Yes | A high level of economic growth | Making sure this country has strong defence fo... | Maintaining order in the nation | Protecting freedom of speech | Progress toward a less impersonal and more hum... | Progress toward a society in which Ideas count... | 8 | 8 | 7 | 5 | 2 | 7 | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | Once a week | Several times a day | Not a religious person | Follow religious norms and ceremonies | Make sense of life after death | 4 | 7 | 2 | 2 | 2 | Never justifiable | Never justifiable | Never justifiable | 4 | 7 | 2 | Never justifiable | Never justifiable | 5 | 3 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Definitely should have the right | Probably should not have the right | Probably should not have the right | Not at all interested | Never | Never | Never | Never | Never | Never | Never | Weekly | Weekly | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Might do | Would never do | Would never do | Would never do | Never | Never | None | 4.0 | 4.0 | Very often | Not at all often | Fairly often | Not often | Not often | Very often | Fairly often | Not often | Not often | Very often | Not very important | Very little | Very good | Very good | Fairly bad | Fairly good | Fairly good | 8 | An essential characteristic of democracy | 7 | An essential characteristic of democracy | An essential characteristic of democracy | 5 | An essential characteristic of democracy | 6 | 8 | An essential characteristic of democracy | Absolutely important | 6 | 6 | Fairly much respect | Quite proud | Very close | Close | Not close at all | Not close at all | Not close at all | Male | 1986.0 | 35.0 | 35-44 | 30-49 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 4 | Yes, own parent(s) | Arabic | Divorced | 1 child | Bachelor or equivalent (ISCED 6) | Higher | Post-secondary non-tertiary education (ISCED 4) | Middle | Bachelor or equivalent (ISCED 6) | Higher | Bachelor or equivalent (ISCED 6) | Higher | Unemployed | NaN | Farm worker (for example: farm labourer, tract... | Farm worker (for example: farm labourer, tract... | Farm worker (for example: farm labourer, tract... | Private non-profit organization | Yes | Just get by | Upper middle class | Seventh step | Medium | Muslim | Islam; nfd | MA: Arabe | Disagree strongly | Disagree | Agree strongly | Disagree | Disagree | Agree strongly | Disagree strongly | Disagree strongly | Agree | Disagree | Disagree | Agree | Agree | Neither agree nor disagree | Agree | Neither agree nor disagree | Agree | Agree strongly | Agree strongly | Agree strongly | Neither agree nor disagree | Disagree | Agree | Agree strongly | Disagree strongly | Neither agree nor disagree | Agree strongly | Agree | Disagree strongly | Agree | Agree | Agree | Disagree | 7 | NaN | NaN | 3 | Mixed | 0 | 0.291389 | 0.249444 | 0.304583 | 0.36 | Low | Low | Very low | 0.11 | Very low | Not religious or atheist | 0.166667 | 0.388889 | Inconformist | Inconformist | Conformist | 0.666667 | Very high | Very high | Very high | 0.0 | No | No | Yes | 0.333333 | Medium | Very low | High | 0.386667 | 0.0 | 0.333333 | 0.666667 | 0.333333 | High | Bajo | 0.165 | 0.165 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 1 | 1 | 1 |
| 3 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070004.0 | 20211112.0 | 202111.0 | 202112.0 | 12.08 | 12.42 | 34.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Very important | Not at all important | Rather important | Very important | Very important | Important | Not mentioned | Important | Not mentioned | Not mentioned | Important | Not mentioned | Not mentioned | Not mentioned | Important | Important | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Agree strongly | Disagree | Disagree | Agree strongly | Agree strongly | Agree | Agree strongly | Agree | Strongly agree | Agree | Agree strongly | Agree strongly | Strongly agree | Strongly agree | Agree strongly | Our present society must be valiantly defended... | Bad thing | Good thing | Good thing | Quite happy | Very good | 8 | Completely dissatisfied | 6 | Never | Rarely | Never | Never | Never | Or about the same | Need to be very careful | Trust completely | Do not trust very much | Do not trust very much | Do not trust at all | Do not trust very much | Do not trust very much | Not very much | A great deal | None at all | None at all | None at all | A great deal | A great deal | Not very much | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | Not very much | Not very much | Not very much | Not very much | Not very much | None at all | None at all | Not very much | None at all | None at all | None at all | Being democratic | India | Geneva | Human rights | Don't belong | Don't belong | Don't belong | Not a member | Not a member | Don't belong | Not a member | Not a member | Don't belong | Don't belong | Don't belong | Don't belong | There should be greater incentives for individ... | Government ownership of business should be inc... | People should take more responsibility to prov... | Competition is harmful | Hard work doesn't generally bring success - it... | Economy growth and creating jobs | 7 | Few of them | Most of them | Few of them | Few of them | Most of them | Frequently | Strongly agree | Very high risk | Quite good | Agree | Disagree | Hard to say | Hard to say | Disagree | Disagree | Agree | Disagree | Place strict limits on the number of foreigner... | Very Secure | Not frequently | Quite frequently | Quite frequently | Not at all frequently | Quite frequently | Not frequently | Very Frequently | Yes | Yes | Yes | Very much | Very much | No | No | Very much | Very much | Very much | Equality | Freedom | Yes | A high level of economic growth | Trying to make our cities and countryside more... | Maintaining order in the nation | Fighting rising prices | A stable economy | Progress toward a society in which Ideas count... | Completely agree | Completely agree | Completely agree | 8 | Completely disagree | A lot better off | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | More than once a week | Several times a day | A religious person | Do good to other people | Make sense of life in this world | Completely agree | 5 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Definitely should have the right | Definitely should not have the right | Definitely should not have the right | Somewhat interested | Occasionally | Less than monthly | Daily | Less than monthly | Daily | Daily | Daily | Daily | Daily | Would never do | Have done | Would never do | Would never do | Have done | Might do | Have done | Have done | Have done | Have done | Have done | Have done | Usually | Usually | None | 4.0 | 4.0 | Not often | Not often | Very often | Very often | Not often | Fairly often | Fairly often | Fairly often | Very often | Fairly often | Very important | Some | Very good | Very bad | Very bad | Very bad | Bad | 5 | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | Not an essential characteristic of democracy | An essential characteristic of democracy | An essential characteristic of democracy | Not an essential characteristic of democracy | Not an essential characteristic of democracy | Absolutely important | 6 | 6 | A great deal of respect | Very proud | Close | Not close at all | Not close at all | Not close at all | Not close at all | Male | 1993.0 | 28.0 | 25-34 | 16-29 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 4 | Yes, own parent(s) | Arabic | Married | 2 children | Primary education (ISCED 1) | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Lower secondary education (ISCED 2) | Lower | Lower secondary education (ISCED 2) | Lower | Unemployed | Full time (30 hours a week or more) | Clerical (for example: secretary, clerk, offic... | Never had a job | Farm worker (for example: farm labourer, tract... | Private business or industry | No | Just get by | Lower middle class | Sixth step | Medium | Muslim | Islam; nfd | MA: Arabe | Agree | Agree strongly | Agree strongly | Agree | Disagree strongly | Neither agree nor disagree | Disagree | Agree strongly | Agree strongly | Agree | Disagree strongly | Disagree | Agree strongly | Agree strongly | Agree strongly | Neither agree nor disagree | Disagree | Disagree | Agree | Agree | Agree | Agree | Agree | Disagree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | 5 | NaN | NaN | 1 | Materialist | -1 | 0.0 | 0.0 | 0.055 | 0.11 | Low | Very low | Very low | 0.0 | Very low | Religious | 0.0 | 0.0 | Conformist | Conformist | Conformist | 0.0 | Very high | Very high | Very high | 0.0 | No | No | No | 0.0 | Low | Very low | High | 0.22 | 0.0 | 0.0 | 0.0 | 0.0 | Very high | Bajo | 0.0 | 0.0 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 1 |
| 4 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504070005.0 | 20211112.0 | 202111.0 | 202112.0 | 13.18 | 13.55 | 37.0 | Paper-and-Pencil Interviewing (PAPI) | MA-09 Souss-Massa | MA: MA-09 Souss-Massa | MA: Afella Ighir | Under 2,000 | Under 5,000 | Another city, town (not a regional or district... | Rural | 99.0 | -8.84 | 29.49 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Rather important | Not very important | Not very important | Very important | Very important | Important | Important | Not mentioned | Important | Not mentioned | Not mentioned | Not mentioned | Important | Not mentioned | Important | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Disagree | Disagree | Agree | Agree strongly | Agree strongly | Agree | Agree strongly | Agree | Neither agree nor disagree | Neither agree nor disagree | Neither agree nor disagree | Agree | Disagree | Disagree | Neither agree nor disagree | The entire way our society is organized must b... | Bad thing | Don't mind | Don't mind | Not very happy | Good | 7 | 7 | 4 | Sometimes | Rarely | Sometimes | Never | Never | Or about the same | Need to be very careful | Trust completely | Do not trust very much | Trust somewhat | Do not trust very much | Do not trust very much | Do not trust very much | Quite a lot | Quite a lot | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | None at all | Not very much | None at all | None at all | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | 5 | China | Washington DC | Destruction of historic monuments | Inactive member | Active member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Don't belong | 6 | 5 | 3 | Competition is good | 5 | Protecting environment | 8 | Most of them | Most of them | Most of them | Most of them | Most of them | Frequently | Strongly disagree | Very high risk | Neither good, nor bad | Agree | Hard to say | Hard to say | Hard to say | Disagree | Agree | Disagree | Hard to say | Let people come as long as there are jobs avai... | Very Secure | Quite frequently | Quite frequently | Not at all frequently | Quite frequently | Quite frequently | Quite frequently | Very Frequently | Yes | No | No | A great deal | Very much | No | No | Very much | Very much | Very much | Equality | Security | No | A high level of economic growth | Making sure this country has strong defence fo... | Maintaining order in the nation | Fighting rising prices | A stable economy | Progress toward a less impersonal and more hum... | 8 | Completely agree | 7 | 6 | Completely disagree | 8 | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | More than once a week | Several times a day | Not a religious person | Follow religious norms and ceremonies | Make sense of life after death | 7 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | 3 | Never justifiable | 4 | Never justifiable | 8 | 2 | Never justifiable | 4 | 5 | Always justifiable | Definitely should have the right | Definitely should not have the right | Definitely should not have the right | Not at all interested | Never | Never | Never | Never | Never | Never | Daily | Daily | Monthly | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Might do | Would never do | Would never do | Would never do | Never | Never | None | 4.0 | 4.0 | Very often | Fairly often | Not at all often | Fairly often | Fairly often | Very often | Fairly often | Not often | Fairly often | Very often | Not very important | Very little | Very bad | Fairly bad | Very bad | Very good | Very good | 7 | Not an essential characteristic of democracy | 3 | 5 | 9 | 6 | An essential characteristic of democracy | An essential characteristic of democracy | 3 | 9 | 8 | 5 | 3 | Not much respect | Not very proud | Close | Close | Not very close | Not very close | Not close at all | Male | 1999.0 | 22.0 | 16-24 | 16-29 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 5 | No | Arabic | Single | No children | Primary education (ISCED 1) | Lower | NaN | NaN | Upper secondary education (ISCED 3) | Middle | Lower secondary education (ISCED 2) | Lower | Full time (30 hours a week or more) | NaN | Service (for example: restaurant owner, police... | NaN | Farm worker (for example: farm labourer, tract... | Private non-profit organization | No | Spent some savings | Working class | Seventh step | Medium | Muslim | Islam; nfd | MA: Arabe | Agree | Disagree | Agree strongly | Agree strongly | Disagree | Disagree strongly | Disagree | Disagree | Agree | Agree | Disagree | Agree | Disagree | Disagree | Agree strongly | Disagree | Disagree | Disagree | Agree strongly | Agree strongly | Disagree | Disagree | Agree | Agree strongly | Disagree strongly | Disagree strongly | Agree strongly | Agree strongly | Disagree strongly | Agree strongly | Agree strongly | Agree | Agree strongly | 2 | 1 | 1 | 1 | Materialist | Determination, perseverance/Independence | 0.3175 | 0.36 | 0.276667 | 0.22 | Medium | High | Very low | 0.386667 | Very low | Not religious or atheist | 0.0 | 0.333333 | Conformist | Conformist | Conformist | 0.0 | High | Low | Low | 0.55 | Yes | No | Yes | 0.666667 | Low | High | High | 0.44 | 0.0 | 0.0 | 0.0 | 0.0 | Very high | Bajo | 0.0 | 0.0 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1195 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504071196.0 | 20211213.0 | 202111.0 | 202112.0 | 14.12 | 15.11 | 59.0 | Paper-and-Pencil Interviewing (PAPI) | MA-02 L'Oriental | MA: MA-02 L'Oriental | MA: Zaio | 20,000-50,000 | 20000-100000 | District center | Urban | 81.0 | -2.73 | 34.94 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Very important | Very important | Very important | Very important | Very important | Important | Important | Important | Important | Not mentioned | Not mentioned | Not mentioned | Important | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Agree strongly | Agree strongly | Agree | Disagree | Agree | Agree | Agree strongly | Agree | Agree strongly | Agree | Disagree | Disagree | Agree | Agree strongly | Strongly agree | Strongly agree | Agree | Our society must be gradually improved by reforms | Bad thing | Good thing | Good thing | Very happy | Very good | A great deal | 6 | 6 | Never | Never | Rarely | Rarely | Never | Better off | Most people can be trusted | Trust completely | Trust somewhat | Trust completely | Trust somewhat | Trust completely | Trust completely | Quite a lot | Quite a lot | Not very much | Not very much | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Not very much | Not very much | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Not very much | Not very much | Being democratic | India | Washington DC | Human rights | Inactive member | Active member | Inactive member | Inactive member | Active member | Don't belong | Inactive member | Inactive member | Don't belong | Don't belong | Don't belong | Don't belong | There should be greater incentives for individ... | 5 | People should take more responsibility to prov... | 5 | 5 | Economy growth and creating jobs | 5 | Few of them | Few of them | Few of them | Few of them | Few of them | Frequently | Disagree | 4 | Quite good | Disagree | Agree | Disagree | Disagree | Disagree | Agree | Disagree | Disagree | Let anyone come who wants to | Quite secure | Not at all frequently | Not at all frequently | Not at all frequently | Not at all frequently | Not at all frequently | Not frequently | Not frequently | Yes | No | No | Very much | Very much | No | No | Very much | Very much | Very much | Freedom | Security | Yes | Seeing that people have more say about how ar... | Making sure this country has strong defence fo... | Giving people more say in important government... | Protecting freedom of speech | Progress toward a less impersonal and more hum... | Progress toward a society in which Ideas count... | 9 | 9 | 9 | 9 | 4 | 8 | Very important | Yes | Yes | Yes | Yes | Disagree | Agree | Never, practically never | Several times each week | Not a religious person | Do good to other people | Make sense of life in this world | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 6 | 6 | 6 | 5 | 5 | 5 | 5 | 4 | 4 | 6 | 5 | 6 | Probably should have the right | Probably should have the right | Probably should have the right | Very interested | Frequently | Never | Weekly | Never | Daily | Monthly | Weekly | Daily | Daily | Have done | Have done | Have done | Have done | Have done | Might do | Might do | Have done | Have done | Have done | Might do | Might do | Always | Always | MAR: National Rally of Independents | MAR: RNI | MAR: Rassemblement National des Independents -... | Very often | Not at all often | Fairly often | Fairly often | Fairly often | Very often | Fairly often | Not often | Very often | Very often | Very important | Some | Fairly Bad | Fairly bad | Fairly bad | Very good | Bad | Right | 7 | 5 | 7 | 6 | 6 | 7 | 6 | 7 | 6 | Absolutely important | Completely democratic | Completely satisfied | Fairly much respect | Quite proud | Close | Close | Close | Close | Close | Male | 1980.0 | 41.0 | 35-44 | 30-49 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 5 | No | Arabic | Single | No children | Post-secondary non-tertiary education (ISCED 4) | Middle | NaN | NaN | Primary education (ISCED 1) | Lower | Primary education (ISCED 1) | Lower | Full time (30 hours a week or more) | NaN | Professional and technical (for example: docto... | NaN | Skilled worker (for example: foreman, motor me... | Private business or industry | Yes | Just get by | Lower middle class | Sixth step | Medium | Muslim | Islam; nfd | MA: Arabe | Agree | Disagree | Disagree | Agree strongly | Disagree | Agree | Agree | Disagree strongly | Agree | Agree | Disagree | Agree | Agree | Disagree | Disagree | Agree | Agree | Agree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Agree | Disagree | Disagree | Agree | Agree | 5 | 4 | 4 | Postmaterialist | Postmaterialist | Determination, perseverance/Independence | 0.526667 | 0.388333 | 0.628796 | 0.424259 | Low | Low | Very low | 0.11 | Very low | Not religious or atheist | 1.0 | 0.666667 | Inconformist | Inconformist | Inconformist | 1.0 | High | High | High | 0.33 | Yes | No | Yes | 0.666667 | Low | Low | High | 0.33 | 0.444444 | 0.555556 | 0.555556 | 0.518519 | Very low | Alto | 1.0 | 1.0 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 1 | 1 | 1 | 1 | 1 |
| 1196 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504071197.0 | 20211213.0 | 202111.0 | 202112.0 | 15.3 | 16.25 | 55.0 | Paper-and-Pencil Interviewing (PAPI) | MA-02 L'Oriental | MA: MA-02 L'Oriental | MA: Zaio | 20,000-50,000 | 20000-100000 | District center | Urban | 81.0 | -2.73 | 34.94 | Arabic | ar | Respondent was somewhat interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Rather important | Rather important | Rather important | Not at all important | Rather important | Rather important | Not mentioned | Important | Important | Not mentioned | Not mentioned | Not mentioned | Important | Important | Not mentioned | Not mentioned | Important | Mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Agree | Agree | Disagree | Disagree | Disagree | Disagree | Neither agree nor disagree | Neither agree nor disagree | Agree strongly | Agree | Disagree | Disagree | Agree | Agree strongly | Strongly agree | Agree | Disagree | The entire way our society is organized must b... | Bad thing | Good thing | Bad thing | Quite happy | Fair | 7 | 6 | 6 | Rarely | Rarely | Rarely | Sometimes | Rarely | Or about the same | Need to be very careful | Do not trust at all | Do not trust at all | Do not trust very much | Do not trust very much | Do not trust very much | Do not trust very much | Not very much | Not very much | Not very much | Not very much | None at all | None at all | None at all | None at all | None at all | None at all | Not very much | Not very much | None at all | Not very much | Not very much | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | 5 | France | Washington DC | Human rights | Inactive member | Active member | Active member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Don't belong | There should be greater incentives for individ... | Government ownership of business should be inc... | 5 | 5 | 8 | Economy growth and creating jobs | 10 There is abundant corruption in my country | All of them | All of them | All of them | All of them | All of them | Always | Agree | 3 | Neither good, nor bad | Agree | Disagree | Agree | Disagree | Agree | Agree | Agree | Agree | Place strict limits on the number of foreigner... | Not very secure | Quite frequently | Quite frequently | Not frequently | Not frequently | Not frequently | Quite frequently | Quite frequently | Yes | Yes | No | A great deal | A great deal | No | No | A good deal | A great deal | A great deal | Freedom | Security | Yes | A high level of economic growth | Seeing that people have more say about how ar... | Giving people more say in important government... | Fighting rising prices | Progress toward a society in which Ideas count... | The fight against crime | Completely agree | Completely agree | Completely agree | Completely agree | 3 | A lot better off | 6 | Yes | Yes | Yes | Yes | Disagree | Agree | Never, practically never | Less often | Not a religious person | Follow religious norms and ceremonies | Make sense of life after death | 5 | 8 | 4 | 4 | 4 | 4 | 4 | 8 | 8 | 6 | 6 | 6 | 4 | 6 | 6 | 4 | 4 | 6 | 4 | 6 | Definitely should not have the right | Definitely should not have the right | Definitely should not have the right | Not at all interested | Never | Never | Never | Never | Daily | Daily | Daily | Daily | Daily | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Never | Never | None | 4.0 | 4.0 | Not at all often | Not often | Very often | Very often | Not at all often | Not at all often | Very often | Very often | Very often | Very often | Very important | Not at all | Very good | Fairly good | Very bad | Very bad | Very bad | Left | An essential characteristic of democracy | 5 | 5 | An essential characteristic of democracy | 5 | An essential characteristic of democracy | An essential characteristic of democracy | 5 | 7 | 5 | 5 | 5 | No respect at all | Very proud | Very close | Very close | Very close | Very close | Very close | Male | 1999.0 | 22.0 | 16-24 | 16-29 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 4 | No | Arabic | Single | No children | Upper secondary education (ISCED 3) | Middle | NaN | NaN | Lower secondary education (ISCED 2) | Lower | Lower secondary education (ISCED 2) | Lower | Student | NaN | Never had a job | NaN | Service (for example: restaurant owner, police... | Private business or industry | No | Just get by | Lower middle class | Sixth step | Medium | Muslim | Islam; nfd | MA: Arabe | Disagree | Agree | Agree | Disagree | Disagree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Agree | Agree | Disagree | Agree | Agree | Agree | Disagree | Agree | Agree | Disagree | Disagree | No trust at all | NaN | NaN | 3 | Mixed | 1 | 0.776667 | 0.61 | 0.518889 | 0.581111 | High | Very low | Low | 0.443333 | Low | Not religious or atheist | 1.0 | 0.776667 | Inconformist | Inconformist | Inconformist | 1.0 | Low | Very low | Very low | 0.886667 | Yes | No | No | 0.333333 | Medium | High | High | 0.606667 | 0.333333 | 0.777778 | 0.555556 | 0.555556 | Low | Medio | 0.58 | 0.58 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 1 | 1 | 0 | 1 | 0 |
| 1197 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504071198.0 | 20211213.0 | 202111.0 | 202112.0 | 16.02 | 17.1 | 68.0 | Paper-and-Pencil Interviewing (PAPI) | MA-02 L'Oriental | MA: MA-02 L'Oriental | MA: Zaio | 20,000-50,000 | 20000-100000 | District center | Urban | 81.0 | -2.73 | 34.94 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Not at all important | Rather important | Not at all important | Rather important | Very important | Important | Not mentioned | Not mentioned | Important | Not mentioned | Not mentioned | Important | Not mentioned | Important | Not mentioned | Important | Mentioned | Not mentioned | Not mentioned | Mentioned | Mentioned | Not mentioned | Mentioned | Mentioned | Not mentioned | Agree strongly | Agree strongly | Disagree | Strongly disagree | Disagree | Agree | Agree | Agree | Agree | Agree | Agree | Agree | Disagree | Agree | Agree | Disagree | Disagree | The entire way our society is organized must b... | Bad thing | Bad thing | Good thing | Quite happy | Fair | 6 | Completely satisfied | 3 | Never | Never | Sometimes | Sometimes | Never | Better off | Most people can be trusted | Trust completely | Trust completely | Trust completely | Trust somewhat | Do not trust very much | Do not trust very much | A great deal | A great deal | Not very much | Quite a lot | None at all | Quite a lot | Quite a lot | Not very much | Not very much | Not very much | Quite a lot | Quite a lot | Not very much | Not very much | Not very much | Not very much | Not very much | None at all | Not very much | Not very much | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | None at all | 4 | France | London | Human rights | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Inactive member | Don't belong | 4 | 8 | 5 | 5 | 9 | Protecting environment | 7 | Most of them | Most of them | Most of them | Most of them | Most of them | Frequently | Agree | 9 | Rather bad | Agree | Disagree | Agree | Disagree | Disagree | Disagree | Agree | Agree | Prohibit people coming here from other countries | Quite secure | Not at all frequently | Not at all frequently | Not at all frequently | Not at all frequently | Not at all frequently | Not at all frequently | Not at all frequently | Yes | Yes | No | Not at all | Not much | No | No | Very much | Very much | Very much | Equality | Security | Yes | A high level of economic growth | Trying to make our cities and countryside more... | Maintaining order in the nation | Fighting rising prices | A stable economy | The fight against crime | 5 | 5 | 5 | 7 | 7 | 5 | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | Never, practically never | Several times a day | A religious person | Do good to other people | Make sense of life in this world | 6 | 9 | 6 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 9 | 5 | 5 | 5 | 5 | 7 | Definitely should not have the right | Probably should not have the right | Probably should not have the right | Not at all interested | Never | Never | Daily | Never | Never | Never | Never | Never | Never | Would never do | Would never do | Would never do | Would never do | Might do | Might do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Never | Never | None | 4.0 | 4.0 | Not at all often | Not often | Very often | Very often | Not at all often | Not at all often | Very often | Very often | Not at all often | Not often | Very important | Not at all | Fairly good | Fairly good | Very bad | Fairly bad | Fairly good | Left | An essential characteristic of democracy | 8 | 4 | An essential characteristic of democracy | 4 | 4 | 4 | 4 | An essential characteristic of democracy | 4 | 5 | 5 | No respect at all | Very proud | Very close | Very close | Very close | Very close | Very close | Female | 1957.0 | 64.0 | 55-64 | 50 and more years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 6 | No | Arabic | Married | 4 children | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Early childhood education (ISCED 0) / no educa... | Lower | Homemaker not otherwise employed | Retired/pensioned | Never had a job | Clerical (for example: secretary, clerk, offic... | Sales (for example: sales manager, shop owner,... | Private business or industry | No | Just get by | Lower middle class | Lower step | Low | Muslim | Islam; nfd | MA: Arabe | Disagree | Agree | Agree | Agree | Disagree | Disagree | Agree | Agree | Agree | Agree | Disagree | Agree | Disagree | Agree | Agree | Disagree | Disagree | Disagree | Agree | Disagree | Disagree | Disagree | Agree | Agree | Disagree | Agree | Agree | Agree | Disagree | Agree | Agree | Disagree | Disagree | 4 | NaN | NaN | Materialist | Materialist | Obedience/Religious Faith | 0.388333 | 0.166667 | 0.270278 | 0.540556 | Low | Very low | Very low | 0.0 | Very low | Religious | 1.0 | 0.333333 | Inconformist | Inconformist | Inconformist | 1.0 | Very high | High | High | 0.22 | No | No | No | 0.0 | Medium-Low | High | Very high | 0.636667 | 0.444444 | 0.444444 | 0.444444 | 0.444444 | Very high | Bajo | 0.0 | 0.0 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 |
| 1198 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504071199.0 | 20211213.0 | 202111.0 | 202112.0 | 17.1 | 18.02 | 52.0 | Paper-and-Pencil Interviewing (PAPI) | MA-02 L'Oriental | MA: MA-02 L'Oriental | MA: Zaio | 20,000-50,000 | 20000-100000 | District center | Urban | 81.0 | -2.73 | 34.94 | Arabic | ar | Respondent was very interested | There were other people around who could follo... | No weighting | 0.833333 | 31083.33333 | Very important | Not very important | Rather important | Not at all important | Very important | Very important | Important | Not mentioned | Important | Important | Not mentioned | Important | Not mentioned | Not mentioned | Not mentioned | Not mentioned | Important | Mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Mentioned | Not mentioned | Agree strongly | Agree strongly | Disagree | Disagree | Agree | Disagree | Agree | Agree | Agree | Agree | Agree | Agree | Disagree | Agree | Agree | Disagree | Disagree | Our society must be gradually improved by reforms | Good thing | Good thing | Good thing | Quite happy | Good | 9 | 6 | 3 | Never | Never | Never | Never | Never | Better off | Need to be very careful | Trust somewhat | Do not trust at all | Do not trust very much | Do not trust at all | Do not trust at all | Do not trust at all | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Not very much | Quite a lot | Quite a lot | None at all | None at all | None at all | Quite a lot | Quite a lot | None at all | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Not very much | Not very much | Not very much | Quite a lot | Not very much | Not very much | Not very much | Quite a lot | Not very much | Not very much | Not very much | 5 | India | Geneva | Human rights | Inactive member | Inactive member | Inactive member | Not a member | Not a member | Don't belong | Not a member | Inactive member | Inactive member | Inactive member | Inactive member | Don't belong | 7 | 7 | 4 | 4 | 4 | Protecting environment | 8 | Most of them | Few of them | Most of them | Few of them | Few of them | Frequently | Strongly agree | 7 | Quite good | Agree | Agree | Disagree | Agree | Disagree | Agree | Agree | Disagree | Place strict limits on the number of foreigner... | Not very secure | Not frequently | Not at all frequently | Not at all frequently | Not frequently | Not at all frequently | Quite frequently | Quite frequently | Yes | Yes | No | Not much | A great deal | No | No | A good deal | A great deal | A great deal | Equality | Security | Yes | A high level of economic growth | Making sure this country has strong defence fo... | Maintaining order in the nation | Protecting freedom of speech | A stable economy | Progress toward a less impersonal and more hum... | 6 | 6 | 4 | 4 | 5 | 7 | 9 | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | Never, practically never | Several times a day | A religious person | Do good to other people | Make sense of life in this world | 7 | 7 | 7 | 4 | 6 | 7 | 7 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 6 | 4 | 4 | 4 | 4 | 6 | Definitely should have the right | Probably should have the right | Probably should have the right | Not at all interested | Never | Never | Weekly | Less than monthly | Weekly | Never | Weekly | Weekly | Monthly | Would never do | Might do | Might do | Might do | Have done | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Would never do | Never | Never | None | 4.0 | 4.0 | Fairly often | Not often | Fairly often | Fairly often | Fairly often | Fairly often | Fairly often | Not often | Fairly often | Fairly often | Very important | Not at all | Very good | Fairly good | Very bad | Fairly bad | Fairly good | Left | 9 | 6 | 4 | 9 | 4 | 9 | 9 | 6 | 9 | 6 | 8 | 3 | No respect at all | Quite proud | Very close | Very close | Very close | Very close | Very close | Female | 1978.0 | 43.0 | 35-44 | 30-49 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 4 | No | Arabic | Married | 2 children | Lower secondary education (ISCED 2) | Lower | Post-secondary non-tertiary education (ISCED 4) | Middle | Primary education (ISCED 1) | Lower | Primary education (ISCED 1) | Lower | Homemaker not otherwise employed | Part time (less than 30 hours a week) | Never had a job | Clerical (for example: secretary, clerk, offic... | Skilled worker (for example: foreman, motor me... | Private business or industry | No | Just get by | Lower middle class | Third step | Low | Muslim | Islam; nfd | MA: Arabe | Disagree | Agree | Agree | Disagree | Disagree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | Agree | Agree | Disagree | Agree | Agree | Agree | Disagree | Agree | Agree | Disagree | Disagree | No trust at all | NaN | NaN | 2 | Mixed | -1 | 0.443333 | 0.221667 | 0.283194 | 0.483889 | Low | Low | Very low | 0.11 | Very low | Religious | 1.0 | 0.333333 | Inconformist | Inconformist | Inconformist | 1.0 | High | High | High | 0.33 | No | No | No | 0.0 | Medium-Low | High | High | 0.523333 | 0.666667 | 0.333333 | 0.333333 | 0.444444 | High | Bajo | 0.165 | 0.165 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 1 | 0 | 1 |
| 1199 | 0.0 | 0.0 | 2021 | Morocco | MAR | Morocco | MOR | 504071200.0 | 20211213.0 | 202111.0 | 202112.0 | 17.4 | 18.13 | 34.0 | Paper-and-Pencil Interviewing (PAPI) | MA-02 L'Oriental | MA: MA-02 L'Oriental | MA: Zaio | 20,000-50,000 | 20000-100000 | District center | Urban | 81.0 | -2.73 | 34.94 | Arabic | ar | Respondent was very interested | There were no other people around who could fo... | No weighting | 0.833333 | 31083.33333 | Very important | Very important | Rather important | Not at all important | Very important | Very important | Important | Not mentioned | Important | Important | Not mentioned | Not mentioned | Important | Important | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Not mentioned | Mentioned | Not mentioned | Mentioned | Not mentioned | Not mentioned | Agree strongly | Disagree | Agree strongly | Disagree | Disagree | Agree strongly | Disagree | Disagree | Agree strongly | Agree | Neither agree nor disagree | Neither agree nor disagree | Agree strongly | Agree strongly | Strongly agree | Strongly agree | Agree strongly | Our society must be gradually improved by reforms | Bad thing | Good thing | Don't mind | Quite happy | Very good | 7 | 8 | 7 | Never | Sometimes | Never | Never | Never | Better off | Need to be very careful | Trust completely | Trust somewhat | Trust completely | Trust somewhat | Trust somewhat | Trust somewhat | Not very much | A great deal | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Not very much | Quite a lot | Quite a lot | Quite a lot | Not very much | Quite a lot | Not very much | Not very much | Not very much | Not very much | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Quite a lot | Not very much | Not very much | 5 | China | Geneva | Destruction of historic monuments | Inactive member | Active member | Active member | Not a member | Inactive member | Don't belong | Inactive member | Inactive member | Inactive member | Inactive member | Active member | Don't belong | There should be greater incentives for individ... | Government ownership of business should be inc... | 8 | Competition is good | In the long run, hard work usually brings a be... | Economy growth and creating jobs | 8 | Most of them | Few of them | Most of them | Most of them | Most of them | Always | Disagree | 4 | Neither good, nor bad | Agree | Agree | Hard to say | Agree | Hard to say | Agree | Hard to say | Agree | Let people come as long as there are jobs avai... | Not very secure | Quite frequently | Very Frequently | Quite frequently | Quite frequently | Very Frequently | Very Frequently | Very Frequently | Yes | No | No | Not much | Very much | No | Yes | A good deal | Not much | Not at all | Freedom | Security | Yes | A high level of economic growth | Making sure this country has strong defence fo... | Maintaining order in the nation | Fighting rising prices | A stable economy | Progress toward a less impersonal and more hum... | Completely agree | Completely agree | 7 | 7 | 3 | 5 | Very important | Yes | Yes | Yes | Yes | Strongly agree | Strongly agree | Less often | Once a year | Not a religious person | Follow religious norms and ceremonies | Make sense of life in this world | 9 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | Never justifiable | 7 | 7 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | 8 | Never justifiable | Never justifiable | Never justifiable | Never justifiable | 8 | Probably should have the right | Definitely should not have the right | Probably should have the right | Somewhat interested | Never | Daily | Never | Daily | Daily | Daily | Daily | Daily | Daily | Would never do | Might do | Might do | Might do | Have done | Would never do | Would never do | Would never do | Have done | Might do | Would never do | Would never do | Never | Never | None | 4.0 | 4.0 | Not often | Not at all often | Very often | Very often | Not often | Not often | Not at all often | Not often | Not often | Not often | Very important | Very little | Fairly Bad | Fairly good | Very bad | Fairly good | Very good | 7 | 8 | 9 | An essential characteristic of democracy | 8 | Not an essential characteristic of democracy | 9 | 7 | 8 | 9 | 9 | 7 | 5 | Fairly much respect | Quite proud | Very close | Close | Close | Not very close | Not very close | Male | 1998.0 | 23.0 | 16-24 | 16-29 years | I am born in this country | Not an immigrant | Not an immigrant | Morocco | Morocco | Morocco | Yes | 3 | No | Arabic | Single | No children | Post-secondary non-tertiary education (ISCED 4) | Middle | NaN | NaN | Lower secondary education (ISCED 2) | Lower | Lower secondary education (ISCED 2) | Lower | Unemployed | NaN | Never had a job | NaN | Service (for example: restaurant owner, police... | Private non-profit organization | No | Just get by | Lower middle class | Seventh step | Medium | Muslim | Islam; nfd | MA: Arabe | Agree | Agree | Agree | Disagree | Disagree | Agree | Disagree strongly | Agree | Agree | Disagree | Disagree strongly | Agree | Agree | Disagree strongly | Agree | Agree | Disagree | Agree | Agree strongly | Agree strongly | Neither agree nor disagree | Disagree | Agree | Agree | Disagree | Disagree | Disagree | Disagree | Disagree | Agree | Agree | Disagree | Disagree | 4 | 2 | 2 | 1 | Materialist | 1 | 0.331944 | 0.443889 | 0.311944 | 0.457222 | Medium | Low | Very low | 0.276667 | Very low | Not religious or atheist | 0.833333 | 0.611111 | Conformist | Conformist | Conformist | 0.0 | Very high | Low | Low | 0.44 | No | No | Yes | 0.333333 | Medium-High | Very low | High | 0.47 | 0.0 | 0.666667 | 0.666667 | 0.444444 | Very high | Bajo | 0.0 | 0.0 | 1.0 | Complete | Complete | Complete | Complete | Complete | Complete | Complete | Complete | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 1 |
1200 rows × 430 columns
variable_view[variable_view['Label'].str.contains('Feeling')]
| Column | Label | Value | |
|---|---|---|---|
| 79 | Q46 | Feeling of happiness | {-5.0: 'Other missing; Multiple answers Mail (... |
variable_view[variable_view['Label'].str.contains('Feeling')].values
array([['Q46', 'Feeling of happiness',
{-5.0: 'Other missing; Multiple answers Mail (EVS)', -4.0: 'Not asked', -2.0: 'No answer', -1.0: "Don't know", 1.0: 'Very happy', 2.0: 'Quite happy', 3.0: 'Not very happy', 4.0: 'Not at all happy'}]],
dtype=object)
print(variable_view[variable_view['Label'].str.contains('education')].values[2])
['Q275' 'Highest educational level: Respondent [ISCED 2011]'
{-5.0: 'Other missing; Multiple answers Mail (EVS)', -4.0: 'Not asked', -3.0: 'Not applicable', -2.0: 'No answer', -1.0: "Don't know", 0.0: 'Early childhood education (ISCED 0) / no education', 1.0: 'Primary education (ISCED 1)', 2.0: 'Lower secondary education (ISCED 2)', 3.0: 'Upper secondary education (ISCED 3)', 4.0: 'Post-secondary non-tertiary education (ISCED 4)', 5.0: 'Short-cycle tertiary education (ISCED 5)', 6.0: 'Bachelor or equivalent (ISCED 6)', 7.0: 'Master or equivalent (ISCED 7)', 8.0: 'Doctoral or equivalent (ISCED 8)'}]
print(variable_view[variable_view['Label'].str.contains('satisfaction', case=False)].values[1])
['Q50' 'Satisfaction with financial situation of household'
{-5.0: 'Missing; Unknown', -4.0: 'Not asked', -2.0: 'No answer', -1.0: "Don't know", 1.0: 'Dissatisfied', 2.0: '2', 3.0: '3', 4.0: '4', 5.0: '5', 6.0: '6', 7.0: '7', 8.0: '8', 9.0: '9', 10.0: 'Satisfied'}]
Frequancies¶
#Frequency of the variable 'Feeling of happiness Q46'
DataFrame = pd.DataFrame({
'Feeling of happiness (LV_Q46)': labeled_df['Q46'],
'Feeling of happiness (NV_Q46)': df['Q46'],
'Highest educational level: Respondent [ISCED 2011] (LV_Q275)': labeled_df['Q275'],
'Highest educational level: Respondent [ISCED 2011] (NV_Q275)': df['Q275'],
'Satisfaction with financial situation of household (LV_Q50)': labeled_df['Q50'],
'Satisfaction with financial situation of household (NV_Q50)': df['Q50'],
})
DataFrame
| Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | Highest educational level: Respondent [ISCED 2011] (LV_Q275) | Highest educational level: Respondent [ISCED 2011] (NV_Q275) | Satisfaction with financial situation of household (LV_Q50) | Satisfaction with financial situation of household (NV_Q50) | |
|---|---|---|---|---|---|---|
| 0 | Quite happy | 2.0 | Primary education (ISCED 1) | 1.0 | 6 | 6.0 |
| 1 | Not at all happy | 4.0 | Bachelor or equivalent (ISCED 6) | 6.0 | 2 | 2.0 |
| 2 | Not very happy | 3.0 | Bachelor or equivalent (ISCED 6) | 6.0 | 3 | 3.0 |
| 3 | Quite happy | 2.0 | Primary education (ISCED 1) | 1.0 | 6 | 6.0 |
| 4 | Not very happy | 3.0 | Primary education (ISCED 1) | 1.0 | 4 | 4.0 |
| ... | ... | ... | ... | ... | ... | ... |
| 1195 | Very happy | 1.0 | Post-secondary non-tertiary education (ISCED 4) | 4.0 | 6 | 6.0 |
| 1196 | Quite happy | 2.0 | Upper secondary education (ISCED 3) | 3.0 | 6 | 6.0 |
| 1197 | Quite happy | 2.0 | Early childhood education (ISCED 0) / no educa... | 0.0 | 3 | 3.0 |
| 1198 | Quite happy | 2.0 | Lower secondary education (ISCED 2) | 2.0 | 3 | 3.0 |
| 1199 | Quite happy | 2.0 | Post-secondary non-tertiary education (ISCED 4) | 4.0 | 7 | 7.0 |
1200 rows × 6 columns
#frequency of the variable 'Feeling of happiness Q46'
Frequency_of_happiness = pd.DataFrame({
'Frequency': DataFrame['Feeling of happiness (LV_Q46)'].value_counts().sort_index(),
'Cumulative Frequency': DataFrame['Feeling of happiness (LV_Q46)'].value_counts().sort_index().cumsum(),
'Percentage': (DataFrame['Feeling of happiness (LV_Q46)'].value_counts(normalize=True)*100).sort_index().round(2),
'Cumulative Percentage': (DataFrame['Feeling of happiness (LV_Q46)'].value_counts(normalize=True).sort_index().cumsum()*100),
}).reset_index()
Frequency_of_happiness = Frequency_of_happiness.merge(pd.DataFrame(DataFrame[['Feeling of happiness (NV_Q46)', 'Feeling of happiness (LV_Q46)']].value_counts().sort_index())
.reset_index(), on='Feeling of happiness (LV_Q46)', how='left').drop(columns='count')
reorder = ['Feeling of happiness (LV_Q46)', 'Feeling of happiness (NV_Q46)', 'Frequency', 'Cumulative Frequency', 'Percentage','Cumulative Percentage']
Frequency_of_happiness = Frequency_of_happiness[reorder]
Frequency_of_happiness
| Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | Frequency | Cumulative Frequency | Percentage | Cumulative Percentage | |
|---|---|---|---|---|---|---|
| 0 | Not at all happy | 4.0 | 11 | 11 | 0.92 | 0.916667 |
| 1 | Not very happy | 3.0 | 195 | 206 | 16.25 | 17.166667 |
| 2 | Quite happy | 2.0 | 845 | 1051 | 70.42 | 87.583333 |
| 3 | Very happy | 1.0 | 149 | 1200 | 12.42 | 100.000000 |
#plot
fig = px.bar(
data_frame= Frequency_of_happiness,
x= 'Feeling of happiness (LV_Q46)',
y= 'Frequency',
title= 'Frequency of the variable "Feeling of happiness Q46"',
hover_data={'Percentage':True}
)
fig.update_layout(
xaxis=dict(
categoryorder='array',
categoryarray=['Early childhood education (ISCED 0) / no education', 'Primary education (ISCED 1)', 'Lower secondary education (ISCED 2)', 'Upper secondary education (ISCED 3)',
'Post-secondary non-tertiary education (ISCED 4)', 'Short-cycle tertiary education (ISCED 5)', 'Bachelor or equivalent (ISCED 6)',
'Master or equivalent (ISCED 7)', 'Doctoral or equivalent (ISCED 8)'],
),
plot_bgcolor='rgba(240, 240, 240, 1)', # Light background color
title_font=dict(size=24, family='Arial', color='black'), # Title font styling
xaxis_title='Frequency',
yaxis_title='Feeling of happiness',
legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)
fig.show()
# frequency of the variable 'Highest educational level: Respondent's Spouse [ISCED 2011]'
Frequency_of_education = pd.DataFrame({
'Frequency': DataFrame['Highest educational level: Respondent [ISCED 2011] (LV_Q275)'].value_counts().sort_index(),
'Cumulative Frequency': DataFrame['Highest educational level: Respondent [ISCED 2011] (LV_Q275)'].value_counts().sort_index().cumsum(),
'Percentage': (DataFrame['Highest educational level: Respondent [ISCED 2011] (LV_Q275)'].value_counts(normalize=True)*100).sort_index().round(2),
'Cumulative Percentage': (DataFrame['Highest educational level: Respondent [ISCED 2011] (LV_Q275)'].value_counts(normalize=True).sort_index().cumsum()*100).round(2),
}).reset_index()
Frequency_of_education
| Highest educational level: Respondent [ISCED 2011] (LV_Q275) | Frequency | Cumulative Frequency | Percentage | Cumulative Percentage | |
|---|---|---|---|---|---|
| 0 | Bachelor or equivalent (ISCED 6) | 81 | 81 | 6.75 | 6.75 |
| 1 | Doctoral or equivalent (ISCED 8) | 4 | 85 | 0.33 | 7.08 |
| 2 | Early childhood education (ISCED 0) / no educa... | 358 | 443 | 29.83 | 36.92 |
| 3 | Lower secondary education (ISCED 2) | 136 | 579 | 11.33 | 48.25 |
| 4 | Master or equivalent (ISCED 7) | 31 | 610 | 2.58 | 50.83 |
| 5 | Post-secondary non-tertiary education (ISCED 4) | 118 | 728 | 9.83 | 60.67 |
| 6 | Primary education (ISCED 1) | 124 | 852 | 10.33 | 71.00 |
| 7 | Short-cycle tertiary education (ISCED 5) | 164 | 1016 | 13.67 | 84.67 |
| 8 | Upper secondary education (ISCED 3) | 184 | 1200 | 15.33 | 100.00 |
# plot
# Creating a bar chart with hover data
fig = px.bar(
Frequency_of_education,
x="Highest educational level: Respondent [ISCED 2011] (LV_Q275)",
y="Frequency",
hover_data={"Percentage": True},
title='Frequency of the variable "Highest educational level: Respondent [ISCED 2011]"',
)
fig.update_layout(
xaxis=dict(
categoryorder='array',
categoryarray=['Early childhood education (ISCED 0) / no education', 'Primary education (ISCED 1)', 'Lower secondary education (ISCED 2)', 'Upper secondary education (ISCED 3)',
'Post-secondary non-tertiary education (ISCED 4)', 'Short-cycle tertiary education (ISCED 5)', 'Bachelor or equivalent (ISCED 6)',
'Master or equivalent (ISCED 7)', 'Doctoral or equivalent (ISCED 8)'],
),
plot_bgcolor='rgba(240, 240, 240, 1)', # Light background color
title_font=dict(size=24, family='Arial', color='black'), # Title font styling
xaxis_title='Frequency',
yaxis_title='Highest educational level: Respondent',
legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)
fig.show()
#frequency of the variable 'Satisfaction with financial situation of household'
Frequency_of_satisfaction = pd.DataFrame({
'Frequency': labeled_df['Q50'].value_counts(),
'Cumulative Frequency': DataFrame['Satisfaction with financial situation of household (LV_Q50)'].value_counts().sort_index().cumsum(),
'Percentage': (DataFrame['Satisfaction with financial situation of household (LV_Q50)'].value_counts(normalize=True)*100).sort_index().round(2),
'Cumulative Percentage': (DataFrame['Satisfaction with financial situation of household (LV_Q50)'].value_counts(normalize=True).sort_index().cumsum()*100),
}).reset_index()
Frequency_of_satisfaction['index'] = Frequency_of_satisfaction['index'].map({'Dissatisfied': 1 , '2':2, '3':3, '4':4, '5':5, '6':6, '7':7, '8':8, '9':9, 'Satisfied':10})
Frequency_of_satisfaction.rename(columns={'index': 'Satisfaction with financial situation of household'}, inplace=True)
Frequency_of_satisfaction
| Satisfaction with financial situation of household | Frequency | Cumulative Frequency | Percentage | Cumulative Percentage | |
|---|---|---|---|---|---|
| 0 | 2 | 25 | 25 | 2.08 | 2.083333 |
| 1 | 3 | 53 | 78 | 4.42 | 6.500000 |
| 2 | 4 | 80 | 158 | 6.67 | 13.166667 |
| 3 | 5 | 199 | 357 | 16.58 | 29.750000 |
| 4 | 6 | 286 | 643 | 23.83 | 53.583333 |
| 5 | 7 | 169 | 812 | 14.08 | 67.666667 |
| 6 | 8 | 183 | 995 | 15.25 | 82.916667 |
| 7 | 9 | 73 | 1068 | 6.08 | 89.000000 |
| 8 | 1 | 42 | 1110 | 3.50 | 92.500000 |
| 9 | 10 | 90 | 1200 | 7.50 | 100.000000 |
# plot
# Creating a bar chart with hover data
fig = px.bar(
Frequency_of_satisfaction,
x='Satisfaction with financial situation of household',
y="Frequency",
hover_data={"Percentage": True},
title='Frequency of the variable "satisfaction with financial situation of household"',
)
fig.update_layout(
xaxis=dict(
categoryorder='array',
categoryarray=['1', '2', '3', '4',
'5', '6', '7',
'8', '9', '10'],
tickmode='array',
tickvals=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], # Ensure all x-axis values are shown
),
plot_bgcolor='rgba(240, 240, 240, 1)', # Light background color
title_font=dict(size=24, family='Arial', color='black'), # Title font styling
xaxis_title='Frequency',
yaxis_title='satisfaction with financial situation of household',
legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)
fig.show()
Now lets see some other plots like violin plot inorder to predict the slope of the correlation (Dependent var: Feeling of happiness Q46, Independent var: Satisfaction with financial situation of household , Highest educational level: Respondent [ISCED 2011])¶
# Create a violing plot
fig = px.violin(
DataFrame,
y= 'Highest educational level: Respondent [ISCED 2011] (LV_Q275)',
x= 'Feeling of happiness (LV_Q46)',
box=True, # Add boxplot overlay
points='all', # Show all data points
title="The Highest educational level based on Feeling of happiness",
)
# Update the order of categories
fig.update_layout(
xaxis=dict(
categoryorder='array',
categoryarray=['Not at all happy', 'Not very happy', 'Quite happy', 'Very happy']
),
yaxis=dict(
categoryorder='array',
categoryarray=['Early childhood education (ISCED 0) / no education', 'Primary education (ISCED 1)', 'Lower secondary education (ISCED 2)', 'Upper secondary education (ISCED 3)',
'Post-secondary non-tertiary education (ISCED 4)', 'Short-cycle tertiary education (ISCED 5)', 'Bachelor or equivalent (ISCED 6)',
'Master or equivalent (ISCED 7)', 'Doctoral or equivalent (ISCED 8)'],
),
plot_bgcolor='rgba(240, 240, 240, 1)', # Light background color
title_font=dict(size=24, family='Arial', color='black'), # Title font styling
xaxis_title='Feeling of happiness',
yaxis_title='Highest educational level: Respondent [ISCED 2011]',
legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)
# Update the violins for styling
fig.update_traces(
width=0.5, # Thinner violins
line_color='black', # Black outline
meanline_visible=True, # Show mean line
scalemode='count', # Adjust width by the count of observations
fillcolor='rgba(0,0,0,0)', # Remove fill
)
# Show the plot
fig.show()
The violin plot reveals that the majority of the population in the sample falls under the category of 'Early childhood education (ISCED 0) / no education,' resulting in a broader base for this category. However, the correlation between the variables remains somewhat unclear.¶
# Create a violing plot
fig = px.violin(
DataFrame,
y= 'Satisfaction with financial situation of household (NV_Q50)',
x= 'Feeling of happiness (LV_Q46)',
box=True, # Add boxplot overlay
points='all', # Show all data points
title="The value of Satisfaction with financial situation of household based on Feeling of happiness",
)
# Update the order of categories
fig.update_layout(
xaxis=dict(
categoryorder='array',
categoryarray=['Not at all happy', 'Not very happy', 'Quite happy', 'Very happy']
),
plot_bgcolor='rgba(240, 240, 240, 1)', # Light background color
title_font=dict(size=24, family='Arial', color='black'), # Title font styling
xaxis_title='Feeling of happiness',
yaxis_title='Satisfaction with financial situation of household',
legend=dict(title='Category', orientation='h', x=0.5, xanchor='center'),
)
# Update the violins for styling
fig.update_traces(
width=0.5, # Thinner violins
line_color='black', # Black outline
meanline_visible=True, # Show mean line
scalemode='count', # Adjust width by the count of observations
fillcolor='rgba(0,0,0,0)', # Remove fill
)
# Show the plot
fig.show()
A clear upward-sloping pattern can be observed for the satisfaction with the financial situation of the household, indicating a positive correlation between satisfaction with the financial situation and the feeling of happiness.¶
Now lets see the correlation matrix¶
#correlation matrix
correlation_matrix = DataFrame[['Feeling of happiness (NV_Q46)','Highest educational level: Respondent [ISCED 2011] (NV_Q275)','Satisfaction with financial situation of household (NV_Q50)']].corr()
correlation_matrix
| Feeling of happiness (NV_Q46) | Highest educational level: Respondent [ISCED 2011] (NV_Q275) | Satisfaction with financial situation of household (NV_Q50) | |
|---|---|---|---|
| Feeling of happiness (NV_Q46) | 1.000000 | -0.005158 | -0.438542 |
| Highest educational level: Respondent [ISCED 2011] (NV_Q275) | -0.005158 | 1.000000 | -0.060551 |
| Satisfaction with financial situation of household (NV_Q50) | -0.438542 | -0.060551 | 1.000000 |
There appears to be an issue, as the correlation between "Satisfaction with financial situation of household (NV_Q50)" and "Feeling of happiness (NV_Q46)" is expected to be positive based on previous observations, but it is displayed as negative in the correlation matrix.¶
# checking the scale
DataFrame.groupby(['Feeling of happiness (LV_Q46)'])['Feeling of happiness (NV_Q46)'].value_counts().reset_index()
| Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | count | |
|---|---|---|---|
| 0 | Not at all happy | 4.0 | 11 |
| 1 | Not very happy | 3.0 | 195 |
| 2 | Quite happy | 2.0 | 845 |
| 3 | Very happy | 1.0 | 149 |
Fixing the "Feeling of Happiness" Scale¶
When I calculated the correlation between Feeling of happiness (LV_Q46) and Satisfaction with financial situation of household (NV_Q50), I got a negative result, which didn’t make sense. After checking the data, I realized that the scale for Feeling of happiness (LV_Q46) was flipped meaning higher numbers showed less happiness, and lower numbers showed more happiness.
To fix this, I created a function to reverse the scale, so that higher numbers now represent more happiness. The new scale works like this:
- 1 → 4
- 2 → 3
- 3 → 2
- 4 → 1
which will be:
- 4: Very happy
- 3: Quite happy
- 2: Not very happy
- 1: Not at all happy
I applied this change and added a new column called Feeling of happiness (NV_Q46)_reversed to the dataset. Now, the values are in the correct order for further analysis.
Here’s the function I used to reverse the scale:
#creating a function for reversed scale
df['inverse_Q46'] = df['Q46'].map({1:4,2:3,3:2,4:1})
DataFrame['Feeling of happiness (NV_Q46)_inverse'] = DataFrame['Feeling of happiness (NV_Q46)'].map({1:4,2:3,3:2,4:1})
DataFrame
| Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | Highest educational level: Respondent [ISCED 2011] (LV_Q275) | Highest educational level: Respondent [ISCED 2011] (NV_Q275) | Satisfaction with financial situation of household (LV_Q50) | Satisfaction with financial situation of household (NV_Q50) | Feeling of happiness (NV_Q46)_inverse | |
|---|---|---|---|---|---|---|---|
| 0 | Quite happy | 2.0 | Primary education (ISCED 1) | 1.0 | 6 | 6.0 | 3 |
| 1 | Not at all happy | 4.0 | Bachelor or equivalent (ISCED 6) | 6.0 | 2 | 2.0 | 1 |
| 2 | Not very happy | 3.0 | Bachelor or equivalent (ISCED 6) | 6.0 | 3 | 3.0 | 2 |
| 3 | Quite happy | 2.0 | Primary education (ISCED 1) | 1.0 | 6 | 6.0 | 3 |
| 4 | Not very happy | 3.0 | Primary education (ISCED 1) | 1.0 | 4 | 4.0 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1195 | Very happy | 1.0 | Post-secondary non-tertiary education (ISCED 4) | 4.0 | 6 | 6.0 | 4 |
| 1196 | Quite happy | 2.0 | Upper secondary education (ISCED 3) | 3.0 | 6 | 6.0 | 3 |
| 1197 | Quite happy | 2.0 | Early childhood education (ISCED 0) / no educa... | 0.0 | 3 | 3.0 | 3 |
| 1198 | Quite happy | 2.0 | Lower secondary education (ISCED 2) | 2.0 | 3 | 3.0 | 3 |
| 1199 | Quite happy | 2.0 | Post-secondary non-tertiary education (ISCED 4) | 4.0 | 7 | 7.0 | 3 |
1200 rows × 7 columns
#correlation matrix
correlation_matrix = DataFrame[['Feeling of happiness (NV_Q46)_inverse','Highest educational level: Respondent [ISCED 2011] (NV_Q275)','Satisfaction with financial situation of household (NV_Q50)']].corr(method='spearman')
correlation_matrix
| Feeling of happiness (NV_Q46)_inverse | Highest educational level: Respondent [ISCED 2011] (NV_Q275) | Satisfaction with financial situation of household (NV_Q50) | |
|---|---|---|---|
| Feeling of happiness (NV_Q46)_inverse | 1.000000 | 0.010114 | 0.437768 |
| Highest educational level: Respondent [ISCED 2011] (NV_Q275) | 0.010114 | 1.000000 | -0.048808 |
| Satisfaction with financial situation of household (NV_Q50) | 0.437768 | -0.048808 | 1.000000 |
# Renaming the correlation matrix
correlation_matrix=correlation_matrix.rename({'Feeling of happiness (NV_Q46)_inverse': 'Feeling of happiness',
'Highest educational level: Respondent [ISCED 2011] (NV_Q275)':'Highest educational level',
'Satisfaction with financial situation of household (NV_Q50)': 'Satisfaction with financial situation of household'},
).rename(columns={'Feeling of happiness (NV_Q46)_inverse': 'Feeling of happiness',
'Highest educational level: Respondent [ISCED 2011] (NV_Q275)':'Highest educational level',
'Satisfaction with financial situation of household (NV_Q50)': 'Satisfaction with financial situation of household'})
correlation_matrix
| Feeling of happiness | Highest educational level | Satisfaction with financial situation of household | |
|---|---|---|---|
| Feeling of happiness | 1.000000 | 0.010114 | 0.437768 |
| Highest educational level | 0.010114 | 1.000000 | -0.048808 |
| Satisfaction with financial situation of household | 0.437768 | -0.048808 | 1.000000 |
# Create a heatmap to visualize the correlations
px.imshow(correlation_matrix,
text_auto=True,
color_continuous_scale='viridis')
# df correlation table
numeric_df = df.select_dtypes(include=['number'])
correlation_matrix_df = numeric_df.corr(method='spearman')
correlation_matrix_df.dropna(axis=1, how='all', inplace=True)
correlation_matrix_df.dropna(axis=0, how='all', inplace=True)
correlation_matrix_df = correlation_matrix_df.drop(['D_INTERVIEW','J_INTDATE','K_TIME_START','K_TIME_END','K_DURATION','N_REGION_ISO','N_REGION_WVS','N_TOWN','G_TOWNSIZE','G_TOWNSIZE2'], axis=0)
correlation_matrix_df = correlation_matrix_df.drop(['D_INTERVIEW','J_INTDATE','K_TIME_START','K_TIME_END','K_DURATION','N_REGION_ISO','N_REGION_WVS','N_TOWN','G_TOWNSIZE','G_TOWNSIZE2'], axis=1)
correlation_matrix_df
| H_SETTLEMENT | H_URBRURAL | I_PSU | O1_LONGITUDE | O2_LATITUDE | E_RESPINT | F_INTPRIVACY | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Q12 | Q13 | Q14 | Q15 | Q16 | Q17 | Q18 | Q19 | Q20 | Q21 | Q22 | Q23 | Q24 | Q25 | Q26 | Q27 | Q28 | Q29 | Q30 | Q31 | Q32 | Q33 | Q33_3 | Q34 | Q34_3 | Q35 | Q35_3 | Q37 | Q38 | Q39 | Q40 | Q41 | Q42 | Q43 | Q44 | Q45 | Q46 | Q47 | Q48 | Q49 | Q50 | Q51 | Q52 | Q53 | Q54 | Q55 | Q56 | Q57 | Q58 | Q59 | Q60 | Q61 | Q62 | Q63 | Q64 | Q65 | Q66 | Q67 | Q68 | Q69 | Q70 | Q71 | Q72 | Q73 | Q74 | Q75 | Q76 | Q77 | Q78 | Q79 | Q80 | Q81 | Q82 | Q82_ARABLEAGUE | Q82_GULFCOOP | Q82_ISLCOOP | Q83 | Q84 | Q85 | Q86 | Q87 | Q88 | Q89 | Q90 | Q91 | Q92 | Q93 | Q94 | Q95 | Q96 | Q97 | Q98 | Q99 | Q100 | Q101 | Q102 | Q103 | Q104 | Q105 | Q106 | Q107 | Q108 | Q109 | Q110 | Q111 | Q112 | Q113 | Q114 | Q115 | Q116 | Q117 | Q118 | Q119 | Q120 | Q121 | Q122 | Q123 | Q124 | Q125 | Q126 | Q127 | Q128 | Q129 | Q130 | Q131 | Q132 | Q133 | Q134 | Q135 | Q136 | Q137 | Q138 | Q139 | Q140 | Q141 | Q142 | Q143 | Q144 | Q145 | Q146 | Q147 | Q148 | Q149 | Q150 | Q151 | Q152 | Q153 | Q154 | Q155 | Q156 | Q157 | Q158 | Q159 | Q160 | Q161 | Q162 | Q163 | Q164 | Q165 | Q166 | Q167 | Q168 | Q169 | Q170 | Q171 | Q172 | Q173 | Q174 | Q175 | Q176 | Q177 | Q178 | Q179 | Q180 | Q181 | Q182 | Q183 | Q184 | Q185 | Q186 | Q187 | Q188 | Q189 | Q190 | Q191 | Q192 | Q193 | Q194 | Q195 | Q196 | Q197 | Q198 | Q199 | Q200 | Q201 | Q202 | Q203 | Q204 | Q205 | Q206 | Q207 | Q208 | Q209 | Q210 | Q211 | Q212 | Q213 | Q214 | Q215 | Q216 | Q217 | Q218 | Q219 | Q220 | Q221 | Q222 | Q223 | Q223_ABREV | Q223_LOCAL | Q224 | Q225 | Q226 | Q227 | Q228 | Q229 | Q230 | Q231 | Q232 | Q233 | Q234 | Q234A | Q235 | Q236 | Q237 | Q238 | Q239 | Q240 | Q241 | Q242 | Q243 | Q244 | Q245 | Q246 | Q247 | Q248 | Q249 | Q250 | Q251 | Q252 | Q253 | Q254 | Q255 | Q256 | Q257 | Q258 | Q259 | Q260 | Q261 | Q262 | X003R | X003R2 | Q270 | Q271 | Q272 | Q273 | Q274 | Q275 | Q275R | Q276 | Q276R | Q277 | Q277R | Q278 | Q278R | Q279 | Q280 | Q281 | Q282 | Q283 | Q284 | Q285 | Q286 | Q287 | Q288 | Q288R | Q289 | Q289CS9 | Q291G1 | Q291G2 | Q291G3 | Q291G4 | Q291G5 | Q291G6 | Q291P1 | Q291P2 | Q291P3 | Q291P4 | Q291P5 | Q291P6 | Q291UN1 | Q291UN2 | Q291UN3 | Q291UN4 | Q291UN5 | Q291UN6 | Q292A | Q292B | Q292C | Q292D | Q292E | Q292F | Q292G | Q292H | Q292I | Q292J | Q292K | Q292L | Q292M | Q292N | Q292O | Q293 | Q294A | Q294B | Y001 | Y002 | Y003 | SACSECVAL | SACSECVALB | RESEMAVAL | RESEMAVALB | I_AUTHORITY | I_NATIONALISM | I_DEVOUT | DEFIANCE | I_RELIGIMP | I_RELIGBEL | I_RELIGPRAC | DISBELIEF | I_NORM1 | I_NORM2 | I_NORM3 | RELATIVISM | I_TRUSTARMY | I_TRUSTPOLICE | I_TRUSTCOURTS | SCEPTICISM | I_INDEP | I_IMAGIN | I_NONOBED | AUTONOMY | I_WOMJOB | I_WOMPOL | I_WOMEDU | EQUALITY | I_HOMOLIB | I_ABORTLIB | I_DIVORLIB | CHOICE | I_VOICE1 | I_VOICE2 | I_VOI2_00 | VOICE | WEIGHT4B | RESEMAVALWGT | Y001_1 | Y001_2 | Y001_3 | Y001_4 | Y001_5 | inverse_Q46 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| H_SETTLEMENT | 1.000000 | 0.851557 | 0.022023 | 0.103847 | -0.116227 | -0.095395 | 0.040084 | -0.038877 | 0.058069 | -0.015620 | 0.034239 | 0.008605 | -0.047738 | 0.045166 | -0.017303 | 0.032499 | -0.006529 | -0.049553 | 0.034531 | 0.007053 | -0.040433 | 0.061857 | -0.117056 | -0.014307 | 0.042674 | -0.027864 | 0.036353 | 0.053876 | 0.020055 | -0.009750 | -0.001316 | 0.119777 | 0.006036 | -0.091690 | -0.081286 | -0.064515 | -0.023122 | -0.085735 | -0.077615 | -0.097049 | -0.089388 | -0.156636 | -0.085014 | -0.112094 | -0.087274 | -0.046805 | -0.136842 | -0.023274 | -0.047734 | 0.027271 | 0.064046 | 0.031853 | 0.024404 | -0.102237 | -0.014024 | 0.009328 | 0.117326 | 0.065581 | 0.063112 | 0.063347 | -0.020556 | -0.007188 | 0.053780 | 0.027629 | -0.071502 | -0.047949 | 0.022692 | -0.003380 | -0.075922 | -0.056697 | -0.048233 | -0.060803 | -0.106181 | -0.122087 | -0.038248 | -0.088293 | -0.065291 | -0.139407 | -0.087272 | 0.000352 | -0.012642 | -0.010987 | -0.043075 | -0.097329 | -0.033689 | -0.093685 | -0.099943 | -0.096002 | -0.120830 | -0.127532 | -0.117685 | -0.117685 | -0.050426 | -0.155186 | -0.100435 | -0.065413 | -0.070906 | -0.097334 | -0.112939 | -0.065259 | -0.054638 | 0.048189 | 0.078257 | 0.041376 | 0.056096 | 0.019676 | 0.082128 | 0.061202 | 0.010528 | -0.007877 | -0.047291 | -0.015795 | 0.023023 | -0.037813 | -0.034872 | 0.019418 | -0.065327 | -0.005596 | 0.105933 | 0.046680 | 0.096061 | 0.085267 | -0.052162 | 0.099447 | -0.118804 | -0.091175 | -0.063991 | -0.110112 | -0.052518 | 0.140583 | 0.056131 | 0.014610 | -0.087602 | -0.055024 | -0.061175 | -0.033641 | -0.056101 | -0.097086 | 0.042975 | 0.050593 | -0.074081 | 0.164338 | -0.052497 | 0.042040 | 0.020126 | 0.032790 | 0.023303 | -0.010018 | 0.016531 | 0.057287 | 0.092492 | 0.070731 | 0.000425 | 0.017357 | -0.086959 | -0.024933 | -0.003775 | -0.032189 | -0.009048 | -0.083949 | -0.074017 | -0.020496 | 0.044733 | -0.029423 | -0.033072 | -0.086645 | 0.073746 | 0.002958 | -0.001183 | 0.057841 | 0.085734 | -0.048577 | 0.014686 | -0.058616 | 0.111462 | 0.077060 | 0.020394 | -0.084883 | -0.046822 | -0.035692 | -0.004019 | -0.087669 | 0.019377 | 0.006953 | 0.087064 | 0.026299 | -0.083116 | 0.061598 | -0.039962 | -0.051815 | 0.029543 | -0.014233 | -0.019275 | -0.016801 | -0.043237 | 0.081821 | 0.091757 | -0.026994 | -0.012886 | 0.077454 | 0.005911 | 0.047798 | 0.005240 | 0.027365 | 0.000759 | 0.026910 | -0.001958 | -0.052647 | -0.038159 | -0.035455 | 0.028039 | 0.049684 | 0.044968 | 0.069813 | 0.040002 | -0.016773 | 0.052238 | 0.025934 | 0.018914 | 0.041979 | 0.035041 | 0.040239 | -0.040038 | 0.009658 | -0.076665 | 0.018833 | 0.066730 | -0.035838 | -0.029597 | 0.007572 | 0.059650 | 0.098316 | -0.049786 | -0.076122 | 0.056362 | 0.056362 | 0.056362 | -0.062370 | 0.012438 | 0.132699 | -0.141543 | -0.131470 | -0.095230 | -0.119461 | 0.037769 | -0.062400 | -0.156662 | -0.019919 | -0.037288 | -0.091421 | -0.200674 | 0.044134 | 0.011035 | -0.034133 | -0.027028 | 0.030852 | 0.111605 | 0.107995 | 0.137522 | 0.073704 | 0.057124 | 0.090475 | 0.142247 | 0.074627 | 0.059050 | -0.083776 | -0.056849 | 0.116047 | -0.032554 | 0.157148 | 0.140267 | 0.158842 | 0.164251 | 0.159950 | 0.000000 | 0.031079 | -0.029442 | -0.030684 | -0.032153 | 0.023859 | 0.054497 | 0.002947 | 0.022724 | -0.010107 | -0.046622 | -0.023183 | -0.006014 | 0.018937 | 0.025835 | 0.077499 | -0.044120 | 0.016296 | -0.006772 | 0.067913 | 0.500790 | 0.406905 | 0.490004 | 0.115498 | 0.060025 | 0.034243 | 0.144766 | 0.039899 | -0.017806 | -0.020394 | 0.020394 | 0.059926 | -0.030383 | -0.008899 | -0.053868 | 0.137074 | 0.011531 | -0.045238 | 0.003709 | 0.060319 | -0.069814 | 0.069633 | -0.021967 | 0.019232 | 0.041862 | 0.031887 | -0.041202 | 0.090315 | -0.047875 | 0.028628 | 0.031524 | 0.077678 | 0.018766 | -0.056070 | -0.050271 | -0.004500 | -0.019040 | 0.049893 | -0.050308 | 0.013474 | -0.020899 | -0.031422 | -0.004306 | 0.009430 | -0.052771 | -0.027919 | -0.030017 | 0.008569 | -0.067567 | 0.039602 | -0.073177 | 0.003240 | -0.020635 | -0.008144 | -0.102237 | -0.032554 | -0.091690 | -0.127415 | -0.047738 | 0.087064 | 0.019377 | 0.057728 | -0.041371 | -0.010698 | -0.002606 | -0.022181 | -0.122087 | -0.139407 | -0.087272 | -0.133369 | 0.017303 | 0.049553 | -0.014307 | 0.021425 | -0.097049 | -0.064515 | -0.023122 | -0.077039 | -0.016801 | 0.081821 | 0.091757 | 0.068763 | -0.085561 | 0.018141 | -0.041872 | -0.039178 | -0.002534 | -0.002534 | 0.031555 | -0.091389 | -0.001225 | 0.034508 | 0.050213 | 0.014024 |
| H_URBRURAL | 0.851557 | 1.000000 | -0.007240 | 0.049131 | -0.159549 | -0.146464 | 0.045706 | -0.066152 | 0.012984 | 0.015860 | 0.045106 | -0.048630 | -0.082980 | 0.051460 | 0.031534 | 0.028702 | 0.019811 | -0.030350 | -0.011287 | -0.000631 | 0.014719 | 0.035223 | -0.085126 | -0.038197 | 0.033675 | -0.019397 | 0.006582 | 0.072326 | -0.005888 | -0.029784 | -0.013945 | 0.062649 | -0.003425 | -0.096293 | -0.112756 | -0.081076 | -0.029527 | -0.093486 | -0.130306 | -0.086858 | -0.067228 | -0.180908 | -0.119982 | -0.121579 | -0.113214 | -0.088097 | -0.130530 | -0.096514 | -0.074104 | 0.002493 | 0.056565 | 0.024946 | 0.021971 | -0.128743 | -0.062452 | -0.003158 | 0.106089 | 0.101900 | 0.100201 | 0.035848 | 0.020130 | -0.005710 | 0.077169 | 0.043365 | -0.054792 | -0.020753 | 0.047417 | -0.002292 | -0.054962 | -0.043850 | -0.023707 | -0.041454 | -0.111633 | -0.063217 | -0.019981 | -0.066508 | -0.062314 | -0.096594 | -0.082381 | 0.004829 | -0.046052 | -0.037641 | -0.038382 | -0.093788 | -0.035681 | -0.080944 | -0.049002 | -0.067615 | -0.114972 | -0.090006 | -0.079432 | -0.079432 | -0.025043 | -0.136474 | -0.073769 | -0.019819 | -0.026779 | -0.066293 | -0.079056 | -0.042096 | -0.009061 | 0.073158 | 0.060044 | 0.037500 | 0.020198 | 0.083122 | 0.131553 | 0.106891 | 0.053467 | 0.035071 | 0.027573 | 0.046850 | 0.079997 | 0.018711 | 0.021914 | 0.080130 | 0.002825 | 0.006671 | 0.134623 | 0.032411 | 0.053654 | 0.048397 | -0.111299 | 0.111312 | -0.071793 | -0.064424 | -0.062487 | -0.038514 | 0.019924 | 0.162838 | 0.062315 | 0.014277 | -0.061955 | -0.012948 | -0.061843 | 0.012064 | -0.041769 | -0.104081 | 0.084169 | 0.064298 | -0.028050 | 0.132473 | -0.127225 | 0.007864 | 0.011539 | 0.001443 | -0.024808 | -0.000485 | 0.025624 | 0.069395 | 0.110462 | 0.086984 | 0.028071 | 0.021805 | -0.070239 | -0.015927 | 0.055380 | -0.042817 | -0.011334 | -0.107514 | -0.032738 | -0.010829 | 0.069389 | -0.001693 | 0.007175 | -0.117339 | 0.095094 | 0.024099 | 0.027504 | 0.094516 | 0.086070 | 0.011821 | 0.064511 | -0.056815 | 0.102266 | 0.116449 | -0.001041 | -0.070600 | -0.057753 | -0.049126 | 0.020457 | -0.112459 | -0.000436 | -0.030402 | 0.096512 | -0.005097 | -0.095316 | 0.052200 | 0.018396 | -0.058054 | 0.075232 | 0.040831 | 0.055036 | 0.049714 | 0.011021 | 0.116482 | 0.086271 | 0.038438 | 0.054477 | 0.150898 | 0.041425 | 0.054914 | 0.100486 | 0.131484 | 0.079563 | 0.123628 | 0.012489 | -0.031203 | -0.004794 | 0.001911 | 0.053618 | 0.093600 | 0.079559 | 0.076254 | 0.028833 | -0.035055 | 0.039582 | 0.007440 | 0.029708 | 0.043042 | 0.063485 | 0.115067 | 0.018557 | 0.063969 | -0.060087 | 0.071730 | 0.138709 | 0.013340 | 0.001112 | 0.045834 | 0.092759 | 0.151268 | 0.011333 | -0.008818 | 0.020189 | 0.020189 | 0.020189 | -0.033240 | -0.018563 | 0.171236 | -0.064148 | -0.084999 | -0.051799 | -0.052544 | 0.073495 | 0.002529 | -0.079020 | 0.004813 | 0.026697 | -0.064529 | -0.227690 | 0.003786 | 0.072098 | -0.102964 | 0.047072 | -0.001015 | 0.150349 | 0.074360 | 0.106199 | 0.131452 | 0.041794 | 0.074449 | 0.118275 | 0.044140 | 0.064365 | -0.045554 | -0.031135 | 0.066632 | -0.026027 | 0.178878 | 0.176302 | 0.193148 | 0.156135 | 0.136599 | 0.000000 | 0.074182 | -0.072280 | -0.069914 | -0.071272 | 0.042041 | 0.077525 | -0.036717 | 0.085190 | -0.062327 | -0.037157 | -0.010115 | 0.018941 | 0.034707 | 0.031865 | 0.080051 | -0.018242 | 0.009398 | 0.015129 | 0.085172 | 0.564214 | 0.477543 | 0.578616 | 0.123015 | 0.059500 | 0.015213 | 0.122976 | 0.103856 | 0.025010 | 0.001041 | -0.001041 | 0.025740 | -0.025222 | 0.032354 | -0.042087 | 0.132103 | -0.022952 | -0.075327 | 0.004824 | 0.071510 | -0.080151 | 0.032829 | -0.045574 | -0.003211 | 0.044208 | 0.041732 | -0.029013 | 0.076128 | -0.064646 | 0.031652 | 0.009586 | 0.087785 | 0.015712 | -0.062487 | -0.059916 | 0.013893 | -0.006317 | 0.061307 | -0.080158 | 0.028021 | -0.042441 | -0.060208 | -0.010541 | 0.008099 | -0.050806 | 0.033583 | 0.036082 | 0.013202 | -0.048473 | -0.023635 | -0.031935 | -0.011192 | -0.038245 | 0.022095 | -0.128743 | -0.026027 | -0.096293 | -0.142248 | -0.082980 | 0.096512 | -0.000436 | 0.048678 | -0.035219 | 0.051686 | 0.056465 | 0.021941 | -0.063217 | -0.096594 | -0.082381 | -0.089319 | -0.031534 | 0.030350 | -0.038197 | -0.026677 | -0.086858 | -0.081076 | -0.029527 | -0.075246 | 0.049714 | 0.116482 | 0.086271 | 0.120450 | -0.072033 | -0.021573 | -0.061652 | -0.058607 | -0.009712 | -0.009712 | -0.010970 | -0.090997 | 0.019165 | 0.069050 | 0.051057 | 0.062452 |
| I_PSU | 0.022023 | -0.007240 | 1.000000 | -0.116754 | 0.306493 | -0.055762 | -0.050132 | -0.039743 | -0.013965 | 0.040565 | -0.075490 | -0.034709 | -0.031265 | -0.050992 | 0.027181 | -0.024489 | 0.101385 | -0.031465 | 0.055939 | 0.056181 | -0.026788 | -0.000256 | -0.034064 | -0.008132 | -0.037549 | 0.012883 | -0.061764 | -0.013970 | -0.011019 | 0.010018 | -0.073382 | 0.004011 | 0.000160 | 0.010783 | 0.010030 | -0.008660 | 0.017324 | 0.054051 | 0.036323 | 0.014687 | 0.001278 | -0.024563 | -0.008668 | -0.006749 | 0.008142 | -0.070051 | -0.037243 | -0.008921 | -0.061041 | -0.053596 | 0.068317 | 0.060913 | 0.040425 | -0.087971 | -0.014992 | 0.033827 | -0.047275 | -0.013006 | -0.022463 | 0.005561 | -0.069875 | -0.026826 | -0.044894 | -0.010940 | -0.017578 | 0.024564 | -0.107387 | -0.040895 | -0.047877 | 0.014598 | 0.035581 | 0.024930 | -0.076984 | -0.131111 | -0.113609 | -0.079528 | -0.089589 | -0.085275 | -0.057321 | -0.072730 | -0.100772 | -0.095451 | -0.084060 | -0.031415 | -0.102039 | -0.109780 | -0.063371 | -0.085419 | -0.037437 | -0.109668 | -0.064256 | -0.064256 | -0.116567 | -0.086413 | -0.062231 | -0.053667 | -0.044835 | -0.041950 | -0.024832 | 0.028672 | 0.015453 | 0.091471 | -0.071911 | 0.043439 | -0.046195 | -0.015310 | -0.049950 | -0.008971 | 0.015952 | 0.002072 | -0.021178 | 0.013816 | -0.025733 | 0.018730 | 0.006858 | 0.006466 | 0.043076 | -0.041861 | -0.020827 | 0.002552 | -0.012857 | -0.028588 | 0.000749 | -0.087641 | -0.053783 | 0.003071 | -0.059687 | -0.055619 | -0.032019 | -0.123415 | 0.068099 | 0.010428 | -0.047292 | 0.046575 | -0.010959 | -0.013679 | -0.004804 | 0.029518 | 0.040639 | -0.008825 | 0.039455 | -0.031709 | 0.075215 | -0.036764 | -0.000539 | -0.017912 | -0.017200 | -0.039915 | -0.024178 | 0.008341 | -0.073806 | -0.061398 | -0.029660 | -0.092242 | -0.041734 | -0.053211 | -0.024009 | -0.069148 | -0.009035 | 0.060695 | 0.020312 | 0.043034 | 0.021196 | 0.003086 | 0.053169 | -0.012684 | 0.004146 | -0.036438 | -0.028653 | -0.030699 | -0.074967 | 0.023510 | 0.017401 | 0.067146 | -0.088533 | -0.076777 | 0.010907 | 0.030447 | 0.062629 | 0.043204 | -0.024067 | 0.053861 | -0.100274 | -0.022787 | -0.056893 | -0.107579 | 0.018800 | 0.061150 | 0.071694 | 0.065845 | 0.078749 | 0.081482 | 0.099417 | 0.088525 | 0.087915 | 0.015189 | -0.071740 | 0.069249 | 0.072434 | 0.089868 | 0.017391 | -0.044231 | 0.047136 | 0.099404 | 0.043335 | 0.049836 | 0.053438 | 0.074952 | 0.072428 | 0.043045 | -0.073443 | -0.045809 | -0.025166 | -0.122479 | -0.040680 | -0.103170 | -0.004947 | -0.110789 | -0.062813 | -0.064972 | -0.117105 | -0.109224 | -0.091461 | -0.062747 | -0.082633 | -0.047662 | -0.069341 | -0.027647 | -0.080280 | -0.077304 | -0.040800 | -0.039954 | -0.083738 | -0.075628 | 0.005067 | 0.005067 | 0.005067 | -0.016354 | 0.025618 | 0.019326 | 0.124905 | 0.025831 | -0.018007 | 0.098679 | -0.019826 | 0.005069 | -0.033980 | -0.090651 | -0.066050 | 0.133294 | 0.112357 | 0.029149 | -0.047982 | 0.058110 | -0.012301 | 0.113349 | -0.052207 | -0.034470 | 0.054531 | -0.009303 | 0.099876 | -0.011070 | -0.062974 | -0.003922 | 0.034221 | 0.124739 | 0.130649 | -0.127336 | -0.054583 | -0.105756 | -0.084470 | -0.099328 | -0.080279 | -0.076487 | 0.000000 | 0.027885 | -0.028227 | -0.028486 | -0.005572 | -0.034786 | 0.071648 | -0.008028 | 0.006807 | -0.018161 | 0.017932 | 0.007251 | -0.015619 | 0.008922 | 0.029219 | -0.014841 | 0.033924 | 0.029998 | 0.048442 | -0.035021 | -0.030544 | -0.036464 | -0.050744 | -0.009806 | -0.006807 | 0.017040 | 0.014977 | -0.064259 | 0.006056 | -0.010907 | 0.010907 | -0.131622 | 0.032855 | 0.129834 | -0.070428 | -0.071205 | -0.051835 | -0.076328 | 0.062956 | 0.091649 | -0.074088 | -0.015633 | -0.024442 | -0.033249 | -0.032604 | 0.063820 | -0.098967 | -0.069951 | -0.043894 | -0.024458 | -0.031574 | 0.090113 | 0.013192 | -0.054915 | 0.000541 | -0.070863 | -0.138224 | -0.058370 | -0.020000 | -0.059310 | -0.011968 | -0.018923 | 0.071282 | 0.010927 | 0.112611 | 0.019271 | -0.003188 | 0.004217 | -0.009062 | -0.005084 | -0.011349 | -0.125562 | -0.015218 | 0.015703 | -0.087971 | -0.054583 | 0.010783 | -0.073563 | -0.031265 | -0.056893 | -0.100274 | -0.092166 | 0.090696 | 0.103247 | 0.121331 | 0.114221 | -0.131111 | -0.085275 | -0.057321 | -0.107812 | -0.027181 | 0.031465 | -0.008132 | -0.010690 | 0.014687 | -0.008660 | 0.017324 | 0.004929 | 0.088525 | 0.015189 | -0.071740 | 0.003571 | -0.024810 | -0.003426 | -0.023455 | -0.024342 | -0.023602 | -0.023602 | 0.001670 | -0.046813 | 0.022416 | -0.014797 | 0.006980 | 0.014992 |
| O1_LONGITUDE | 0.103847 | 0.049131 | -0.116754 | 1.000000 | 0.722242 | -0.059849 | 0.005890 | 0.040916 | 0.076138 | -0.047052 | 0.092466 | 0.048002 | 0.054862 | 0.025678 | -0.053052 | -0.007246 | 0.111024 | -0.043464 | 0.042924 | 0.026756 | -0.052537 | 0.157427 | -0.038824 | -0.077794 | 0.021990 | 0.001056 | 0.158999 | 0.096881 | 0.123343 | -0.017235 | 0.036344 | 0.156647 | 0.031219 | 0.063570 | 0.077015 | 0.059615 | -0.069605 | -0.055319 | -0.015729 | 0.027118 | -0.016016 | 0.019567 | 0.049462 | -0.075790 | -0.042502 | 0.087676 | 0.034470 | 0.106787 | 0.097361 | 0.133679 | 0.069490 | 0.018462 | -0.085503 | 0.001610 | -0.096979 | 0.047826 | 0.053204 | 0.148501 | 0.100278 | 0.050238 | -0.029394 | 0.102556 | 0.054321 | 0.055253 | 0.073835 | -0.038564 | 0.095919 | 0.128707 | 0.110858 | 0.064932 | 0.091128 | 0.101633 | 0.125377 | 0.020622 | 0.050030 | 0.044035 | 0.069107 | -0.005397 | 0.034161 | 0.027586 | 0.060376 | 0.080412 | 0.027352 | 0.029976 | 0.029735 | 0.031818 | 0.007153 | 0.012391 | 0.045406 | 0.026420 | 0.041899 | 0.041899 | 0.036367 | 0.049831 | 0.044453 | 0.009573 | 0.024816 | 0.006541 | 0.000703 | -0.007032 | 0.051872 | 0.029951 | -0.060673 | -0.183401 | -0.041580 | -0.061439 | -0.025494 | -0.043583 | -0.088333 | -0.090097 | -0.075942 | -0.067395 | -0.043390 | -0.069375 | -0.033253 | -0.033004 | 0.025731 | 0.030621 | 0.105126 | 0.022141 | -0.098930 | -0.059983 | 0.046854 | 0.039574 | 0.083208 | 0.048457 | 0.095436 | 0.095573 | 0.038452 | -0.019340 | -0.062291 | 0.093713 | 0.113991 | -0.047929 | 0.048296 | -0.119706 | -0.004532 | -0.070132 | 0.041083 | -0.024455 | -0.080336 | -0.080137 | -0.057402 | 0.148599 | 0.182667 | 0.003813 | 0.058313 | 0.125958 | 0.105388 | 0.110219 | 0.048692 | 0.088093 | 0.017225 | 0.088011 | 0.114540 | 0.025180 | 0.003396 | 0.172231 | 0.136028 | 0.067735 | -0.079576 | -0.037607 | -0.018276 | -0.119099 | 0.072351 | -0.147336 | 0.088718 | -0.062293 | -0.021589 | 0.115776 | 0.166657 | 0.052051 | 0.046029 | -0.058014 | 0.122322 | -0.097843 | 0.055898 | -0.024621 | -0.001673 | 0.012886 | 0.009962 | 0.002914 | 0.099472 | -0.016549 | 0.022025 | -0.020535 | 0.001965 | -0.017877 | -0.061789 | -0.134043 | -0.103480 | -0.095140 | -0.132029 | -0.120647 | -0.096566 | -0.040790 | -0.087696 | -0.085380 | -0.106965 | -0.060880 | -0.159537 | -0.140192 | -0.172999 | -0.157799 | -0.171414 | -0.169220 | -0.192920 | 0.067324 | -0.034810 | -0.049142 | 0.039028 | 0.013217 | 0.013950 | 0.044254 | 0.068019 | 0.057231 | -0.000463 | -0.001730 | -0.012831 | 0.146521 | 0.129855 | 0.112746 | 0.017245 | 0.022790 | 0.031638 | 0.104453 | 0.055863 | 0.072978 | 0.006945 | 0.114086 | 0.060013 | 0.006246 | 0.008200 | -0.007043 | -0.052169 | -0.052169 | -0.052169 | 0.116965 | 0.040416 | -0.019283 | -0.012083 | 0.096722 | 0.074968 | -0.028549 | -0.102809 | 0.070331 | 0.023407 | -0.053729 | 0.205284 | -0.090511 | -0.011448 | 0.031098 | -0.060291 | -0.012437 | 0.020516 | 0.010807 | -0.004067 | -0.006007 | -0.010135 | -0.022925 | 0.030758 | 0.071994 | -0.064756 | -0.002962 | 0.072563 | 0.030148 | 0.004407 | 0.063031 | 0.095523 | 0.092697 | 0.043078 | 0.079558 | 0.078885 | 0.077140 | 0.000000 | -0.028418 | 0.027831 | 0.030710 | 0.030255 | -0.041624 | -0.186009 | -0.182453 | -0.056417 | -0.003473 | 0.109699 | 0.116146 | 0.117406 | 0.141208 | 0.086385 | 0.073428 | 0.073643 | 0.086589 | -0.152446 | -0.043940 | 0.024752 | -0.033762 | -0.016919 | -0.100949 | -0.032652 | -0.078910 | -0.090938 | 0.073038 | 0.089554 | -0.055898 | 0.055898 | 0.001538 | 0.105672 | 0.036352 | -0.043524 | -0.116459 | -0.057956 | 0.042890 | 0.062975 | 0.095493 | -0.011585 | -0.095488 | -0.063279 | 0.155522 | 0.095380 | 0.045533 | 0.053513 | -0.071893 | -0.013968 | -0.086525 | -0.064361 | -0.133592 | -0.094198 | 0.078507 | 0.076468 | -0.051110 | -0.016485 | 0.080811 | 0.007910 | 0.006752 | 0.035578 | 0.048494 | -0.006921 | -0.055749 | -0.013324 | 0.040492 | 0.024857 | 0.066688 | -0.003209 | 0.069271 | -0.039879 | 0.086671 | -0.036644 | -0.052619 | 0.001610 | 0.095523 | 0.063570 | 0.068971 | 0.054862 | 0.022025 | 0.099472 | 0.079998 | -0.139070 | -0.100646 | -0.125564 | -0.138864 | 0.020622 | -0.005397 | 0.034161 | 0.017956 | 0.053052 | 0.043464 | -0.077794 | 0.006317 | 0.027118 | 0.059615 | -0.069605 | 0.007440 | -0.120647 | -0.040790 | -0.087696 | -0.113841 | 0.004226 | 0.029244 | 0.024145 | 0.021002 | 0.006185 | 0.006185 | 0.061703 | 0.025094 | -0.025109 | 0.057092 | 0.060704 | 0.096979 |
| O2_LATITUDE | -0.116227 | -0.159549 | 0.306493 | 0.722242 | 1.000000 | -0.057714 | -0.048466 | 0.030617 | 0.026365 | 0.010427 | 0.018277 | 0.013106 | 0.015727 | -0.021278 | -0.019870 | -0.022647 | 0.161237 | -0.030911 | 0.044031 | 0.013569 | -0.035372 | 0.076845 | -0.006057 | -0.033856 | -0.032888 | 0.021261 | 0.081593 | 0.056054 | 0.085573 | 0.019782 | -0.034462 | 0.104237 | 0.022075 | 0.063446 | 0.090255 | 0.071491 | -0.033332 | 0.014179 | -0.005217 | 0.080812 | 0.020683 | 0.051093 | 0.044425 | -0.006098 | -0.016498 | 0.048557 | 0.032853 | 0.089007 | 0.065746 | 0.062297 | 0.052924 | 0.037360 | -0.079420 | -0.008941 | -0.058092 | 0.043269 | 0.012857 | 0.096540 | 0.046587 | 0.032090 | -0.049095 | 0.032642 | -0.015833 | 0.040282 | 0.010098 | -0.003727 | -0.005594 | 0.031509 | 0.045553 | 0.029078 | 0.054946 | 0.063292 | 0.082379 | -0.020670 | 0.003755 | 0.027425 | 0.022470 | -0.023306 | 0.030919 | -0.011493 | 0.007841 | 0.028263 | -0.008034 | 0.010184 | -0.022825 | -0.004155 | 0.022087 | 0.001762 | 0.031876 | -0.008106 | 0.013948 | 0.013948 | -0.009841 | 0.025440 | 0.034899 | -0.011395 | 0.018948 | 0.021081 | 0.022163 | 0.026402 | 0.039985 | 0.087884 | -0.093887 | -0.116238 | -0.052151 | -0.068917 | -0.064491 | -0.064299 | -0.068030 | -0.083540 | -0.057360 | -0.043706 | -0.057687 | -0.051953 | -0.021425 | -0.048257 | 0.012493 | -0.025869 | 0.089801 | 0.030490 | -0.070871 | -0.052063 | 0.041149 | -0.045277 | 0.032087 | 0.050405 | 0.021414 | 0.030795 | 0.024061 | -0.127352 | -0.048963 | 0.105882 | 0.068656 | -0.018468 | 0.081628 | -0.065613 | 0.024147 | 0.013874 | 0.043830 | -0.036618 | -0.019808 | -0.098844 | -0.022182 | 0.089683 | 0.133593 | 0.002843 | 0.034406 | 0.097545 | 0.083250 | 0.089950 | 0.023429 | 0.064393 | -0.016461 | 0.029382 | 0.087218 | -0.006579 | -0.002664 | 0.118318 | 0.110540 | 0.089574 | 0.008369 | -0.025078 | 0.010500 | -0.075447 | 0.077393 | -0.061683 | 0.046905 | -0.048148 | -0.023386 | 0.016402 | 0.041475 | 0.043088 | 0.033111 | 0.003343 | 0.044711 | -0.144469 | 0.051913 | 0.039219 | 0.027092 | 0.038489 | 0.046304 | 0.097639 | -0.017156 | -0.041290 | -0.039234 | -0.063902 | 0.012396 | 0.005655 | 0.009821 | -0.061007 | -0.036879 | -0.006779 | -0.025127 | -0.030043 | -0.018368 | -0.041048 | -0.136900 | -0.021118 | -0.046323 | -0.026595 | -0.097077 | -0.141518 | -0.095271 | -0.073440 | -0.108562 | -0.107955 | -0.134758 | 0.091358 | -0.028428 | -0.025500 | -0.034123 | -0.022712 | -0.012131 | -0.048510 | 0.013224 | -0.011128 | -0.035711 | -0.066759 | -0.054486 | 0.069473 | 0.045858 | 0.044780 | -0.000801 | 0.020279 | 0.015235 | 0.061838 | 0.001122 | 0.061915 | 0.018295 | 0.076546 | 0.030728 | -0.034531 | -0.038919 | -0.028224 | -0.038414 | -0.038414 | -0.038414 | 0.076998 | 0.047612 | -0.012989 | 0.083369 | 0.114979 | 0.073140 | 0.047906 | -0.069170 | 0.066470 | -0.021381 | -0.028275 | 0.116606 | 0.004681 | 0.080791 | 0.018844 | -0.066952 | 0.031385 | 0.028726 | 0.040409 | -0.033413 | -0.085949 | -0.012566 | -0.042934 | 0.038288 | 0.013065 | -0.099399 | -0.034321 | 0.029875 | 0.092969 | 0.074105 | -0.030882 | 0.015356 | -0.036587 | -0.054650 | -0.024572 | -0.009361 | -0.004602 | 0.000000 | -0.020769 | 0.020958 | 0.027201 | 0.029743 | -0.028287 | -0.086147 | -0.290074 | -0.034140 | -0.003928 | 0.067201 | 0.057421 | 0.022138 | 0.054537 | 0.037032 | 0.002902 | 0.028138 | 0.024046 | -0.082991 | -0.039814 | -0.092469 | -0.163632 | -0.121836 | -0.075421 | -0.058499 | -0.028549 | -0.071411 | -0.017564 | 0.054231 | -0.051913 | 0.051913 | -0.070618 | 0.116182 | 0.097418 | -0.029474 | -0.146316 | -0.088151 | 0.008134 | 0.054544 | 0.073537 | 0.004754 | -0.086937 | -0.065128 | 0.067892 | 0.064300 | 0.064044 | -0.028200 | -0.114672 | -0.026744 | -0.065276 | -0.061806 | -0.070525 | -0.052957 | 0.053965 | 0.053237 | -0.092123 | -0.053820 | 0.069863 | 0.028792 | -0.035579 | 0.042097 | 0.058990 | 0.020303 | -0.055008 | 0.049957 | 0.031032 | 0.018705 | 0.050419 | 0.007685 | 0.038677 | -0.016867 | -0.024675 | -0.015254 | -0.017894 | -0.008941 | 0.015356 | 0.063446 | 0.024643 | 0.015727 | -0.039234 | -0.017156 | -0.027479 | -0.051794 | 0.003037 | -0.012813 | -0.026162 | -0.020670 | -0.023306 | 0.030919 | -0.008039 | 0.019870 | 0.030911 | -0.033856 | 0.006538 | 0.080812 | 0.071491 | -0.033332 | 0.044793 | -0.030043 | -0.041048 | -0.136900 | -0.101487 | 0.010297 | 0.016982 | 0.017539 | 0.015430 | -0.029156 | -0.029156 | 0.037396 | 0.016643 | -0.012550 | 0.033516 | 0.039279 | 0.058092 |
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| Y001_2 | -0.091389 | -0.090997 | -0.046813 | 0.025094 | 0.016643 | 0.063428 | 0.019789 | 0.132337 | 0.044242 | 0.025408 | -0.049278 | 0.143255 | 0.168313 | 0.026434 | 0.019749 | 0.076745 | 0.029600 | -0.120723 | -0.093858 | -0.081154 | 0.018967 | 0.113520 | 0.025399 | 0.027071 | 0.035880 | -0.004006 | 0.006260 | 0.063080 | 0.119588 | 0.040729 | 0.030653 | 0.067890 | 0.002696 | 0.148260 | 0.112155 | 0.038635 | -0.015322 | 0.087535 | 0.103538 | 0.061580 | 0.079453 | 0.067376 | -0.015217 | 0.124271 | 0.194607 | 0.149493 | 0.158045 | 0.156920 | 0.195505 | 0.137440 | -0.114491 | -0.256509 | 0.054434 | 0.100253 | 0.035201 | -0.044787 | -0.112508 | -0.099720 | -0.021884 | -0.192483 | -0.117456 | -0.145231 | -0.121965 | -0.137070 | -0.050947 | -0.175582 | 0.075986 | 0.020264 | 0.037274 | -0.075929 | -0.075766 | -0.065082 | -0.011072 | 0.049266 | 0.026325 | 0.059690 | 0.067607 | 0.061791 | 0.034226 | 0.068153 | 0.059792 | 0.062411 | 0.066859 | 0.081838 | 0.050388 | 0.046997 | 0.057224 | 0.110943 | 0.070478 | 0.085928 | 0.061468 | 0.061468 | 0.010447 | 0.093112 | 0.030717 | -0.006200 | 0.009829 | 0.013945 | 0.018276 | 0.031186 | 0.015441 | -0.035454 | 0.036683 | -0.014390 | -0.000418 | 0.027413 | 0.028147 | 0.030440 | 0.058288 | 0.067006 | 0.053873 | 0.039691 | 0.052461 | 0.074742 | 0.056996 | 0.040806 | 0.054845 | -0.048373 | -0.001657 | 0.082514 | 0.042155 | 0.036184 | -0.018024 | 0.007062 | 0.108921 | 0.105169 | 0.109948 | 0.118744 | 0.031875 | -0.028478 | -0.004838 | 0.024223 | 0.116391 | 0.096292 | 0.062201 | -0.018373 | 0.082355 | -0.046521 | 0.009617 | -0.135649 | 0.009585 | -0.144752 | -0.055729 | -0.021749 | -0.036591 | -0.141356 | -0.154521 | 0.004819 | -0.038371 | -0.089002 | -0.049406 | 0.017080 | -0.028910 | 0.040719 | -0.029966 | 0.034374 | -0.029987 | 0.074619 | 0.045042 | -0.015815 | -0.069374 | -0.064073 | 0.043387 | 0.068626 | 0.038842 | 0.358239 | -0.033686 | 0.196343 | -0.059298 | -0.077353 | -0.056731 | -0.015004 | -0.022532 | 0.043422 | -0.038338 | -0.151016 | 0.076888 | 0.100832 | 0.141944 | 0.117464 | 0.113298 | 0.137052 | 0.069011 | 0.071861 | 0.002535 | -0.091622 | -0.015155 | -0.016082 | 0.004428 | 0.025860 | 0.050675 | 0.102127 | 0.080114 | 0.105816 | 0.111074 | 0.088308 | -0.044079 | 0.092925 | 0.108864 | 0.090367 | 0.035811 | -0.047491 | 0.079873 | 0.104941 | 0.129818 | 0.055795 | -0.064004 | 0.029527 | -0.032192 | -0.026692 | -0.042081 | 0.004429 | -0.196878 | 0.050488 | -0.025545 | -0.029979 | -0.166636 | -0.056847 | -0.045624 | -0.027233 | -0.038936 | -0.010272 | -0.041127 | -0.066759 | -0.001779 | -0.043159 | -0.098500 | -0.135199 | 0.020870 | -0.098447 | -0.168127 | -0.170025 | 0.054586 | 0.032361 | -0.007692 | -0.007692 | -0.007692 | 0.023774 | -0.115416 | -0.079483 | -0.003472 | 0.072164 | 0.113293 | 0.018805 | -0.111240 | 0.103774 | 0.081637 | 0.072020 | -0.043247 | -0.026671 | 0.018234 | -0.060888 | 0.070720 | -0.061791 | -0.015806 | -0.089231 | 0.024876 | -0.067404 | -0.143897 | 0.001534 | -0.074222 | -0.064329 | -0.020025 | 0.014504 | -0.089341 | 0.078439 | 0.041685 | -0.028475 | 0.064506 | 0.114330 | 0.100256 | 0.090030 | 0.061293 | 0.017037 | -0.041963 | 0.029728 | -0.030708 | -0.037916 | -0.032647 | 0.026022 | -0.020747 | -0.060929 | 0.031534 | -0.035624 | 0.051114 | 0.043886 | 0.035231 | 0.026624 | 0.109157 | 0.065950 | 0.118895 | 0.040221 | 0.027691 | 0.044968 | -0.044228 | -0.045498 | -0.046676 | 0.017408 | -0.008562 | 0.020592 | -0.108104 | 0.037266 | 0.043586 | -0.076888 | 0.076888 | 0.031312 | 0.045968 | 0.015818 | 0.013768 | -0.051662 | 0.038906 | 0.035458 | 0.039204 | 0.030943 | 0.029906 | -0.048844 | -0.000611 | 0.021623 | -0.030694 | -0.055951 | 0.014598 | -0.051070 | 0.002487 | -0.104176 | -0.009634 | -0.008382 | 0.006443 | 0.071902 | 0.054432 | 0.114754 | 0.164731 | 0.099927 | 0.040694 | 0.073795 | 0.061840 | -0.037609 | 0.078611 | -0.019522 | -0.010363 | 0.152678 | 0.116025 | 0.356955 | 0.507925 | 0.025800 | 0.130296 | 0.112968 | 0.284264 | 0.041551 | 0.100253 | 0.064506 | 0.148260 | 0.148920 | 0.168313 | 0.002535 | 0.069011 | 0.063537 | 0.034219 | 0.074349 | 0.059138 | 0.061665 | 0.049266 | 0.061791 | 0.034226 | 0.063844 | -0.019749 | 0.120723 | 0.027071 | 0.039918 | 0.061580 | 0.038635 | -0.015322 | 0.034356 | 0.105816 | 0.088308 | -0.044079 | 0.064919 | 0.772435 | 0.100143 | 0.534206 | 0.532122 | 0.009767 | 0.009767 | 0.075052 | 1.000000 | -0.226490 | 0.131468 | 0.047120 | -0.035201 |
| Y001_3 | -0.001225 | 0.019165 | 0.022416 | -0.025109 | -0.012550 | -0.064229 | 0.023036 | 0.031921 | -0.068211 | -0.077342 | -0.011072 | 0.018946 | 0.015069 | -0.022669 | -0.073659 | -0.073739 | 0.026942 | -0.020951 | 0.038834 | 0.063403 | 0.037486 | -0.060708 | -0.005555 | 0.054936 | 0.033502 | -0.014629 | 0.007146 | 0.025890 | 0.031732 | 0.000385 | -0.007686 | 0.012294 | -0.013807 | -0.065446 | -0.035466 | -0.010100 | -0.034609 | -0.081064 | -0.086399 | -0.015689 | -0.022618 | -0.044360 | -0.045807 | -0.083277 | -0.052140 | -0.070279 | 0.032906 | -0.028192 | -0.081894 | 0.026132 | 0.037312 | 0.036292 | -0.001792 | -0.017982 | -0.060032 | 0.017699 | 0.058977 | 0.056430 | -0.001970 | 0.005081 | -0.017050 | 0.038656 | 0.000748 | 0.012819 | -0.063619 | 0.006075 | -0.034932 | 0.071323 | 0.064879 | 0.048127 | 0.035112 | 0.034041 | 0.054384 | -0.027351 | 0.033970 | 0.054055 | 0.022710 | -0.034520 | 0.076224 | 0.009160 | 0.034645 | 0.009011 | -0.017608 | -0.021320 | 0.034911 | 0.052744 | 0.000984 | -0.022568 | 0.002707 | -0.021976 | 0.037489 | 0.037489 | 0.024737 | 0.042529 | 0.069755 | 0.078815 | 0.082162 | 0.052112 | 0.055029 | 0.029847 | 0.085013 | 0.099460 | 0.076523 | 0.035469 | 0.007328 | 0.037966 | 0.050826 | 0.030787 | 0.029681 | 0.055781 | 0.034471 | 0.039936 | 0.040578 | 0.047486 | 0.042941 | 0.079414 | 0.045216 | 0.050585 | -0.012495 | 0.042996 | -0.038668 | -0.033042 | -0.027400 | 0.161877 | 0.068050 | 0.046792 | 0.052881 | 0.037720 | 0.026993 | 0.059696 | 0.031785 | -0.017022 | -0.060523 | 0.051844 | -0.036880 | -0.034560 | 0.021461 | 0.053591 | 0.027919 | -0.018738 | -0.041430 | -0.064675 | 0.060074 | -0.051432 | -0.056615 | -0.096191 | -0.055148 | -0.121767 | -0.034218 | -0.058519 | 0.002341 | -0.000948 | 0.015786 | -0.079948 | 0.044160 | -0.047721 | 0.035219 | 0.034413 | 0.077619 | 0.106007 | -0.049841 | -0.069430 | -0.032878 | -0.019585 | 0.045569 | -0.038920 | 0.296133 | -0.028258 | -0.023439 | 0.092932 | 0.046399 | 0.049951 | 0.002607 | -0.119958 | 0.057898 | -0.054884 | -0.056764 | -0.001390 | -0.032855 | -0.043266 | 0.023448 | 0.073621 | -0.065925 | -0.013990 | 0.033534 | 0.071278 | 0.108026 | -0.036065 | 0.056643 | -0.051341 | -0.007110 | -0.041349 | -0.042090 | 0.051986 | 0.034771 | 0.056003 | -0.005580 | 0.092087 | 0.074502 | 0.070803 | -0.092010 | -0.107924 | -0.019077 | -0.009048 | -0.018035 | -0.041778 | -0.032778 | 0.085964 | 0.160266 | 0.211696 | -0.095349 | -0.134629 | -0.006811 | -0.079482 | -0.064583 | -0.096408 | -0.003174 | -0.082349 | -0.033829 | -0.043901 | -0.077744 | -0.043512 | -0.130850 | -0.095486 | -0.108772 | -0.112929 | -0.116091 | 0.021243 | -0.126567 | -0.083872 | -0.022812 | -0.077693 | -0.026407 | -0.017065 | 0.003051 | 0.003051 | 0.003051 | 0.090552 | 0.045761 | -0.059515 | -0.020068 | 0.040046 | 0.018093 | -0.063641 | -0.024479 | -0.004037 | -0.014834 | -0.036442 | -0.025847 | 0.050726 | -0.012855 | 0.008063 | -0.012923 | 0.007810 | 0.001551 | 0.066856 | 0.003584 | 0.024550 | 0.061189 | 0.028995 | 0.080540 | 0.000034 | -0.007669 | -0.050519 | 0.065830 | -0.032752 | -0.049417 | 0.055005 | 0.064964 | 0.032589 | 0.060934 | 0.021988 | 0.081971 | 0.084485 | -0.014273 | 0.055878 | -0.055864 | -0.047452 | -0.055882 | -0.031061 | 0.050776 | 0.048065 | 0.034065 | -0.067343 | 0.061214 | 0.057296 | 0.057430 | 0.060120 | 0.057225 | 0.015883 | 0.063633 | 0.024822 | -0.030799 | -0.041713 | 0.015390 | -0.039673 | -0.050493 | 0.009155 | -0.030830 | -0.062569 | -0.054289 | 0.028213 | 0.030376 | 0.056764 | -0.056764 | 0.062305 | -0.036093 | -0.043499 | 0.059705 | 0.035251 | 0.051477 | 0.003928 | -0.009906 | 0.036914 | 0.068186 | 0.051795 | 0.052858 | 0.106206 | -0.049190 | 0.032295 | 0.033354 | 0.025812 | 0.073422 | -0.038018 | -0.026188 | -0.076706 | -0.058834 | -0.052557 | 0.049824 | -0.062707 | -0.035001 | 0.007624 | 0.037265 | -0.045064 | 0.030933 | 0.051170 | -0.029699 | -0.029650 | 0.014263 | 0.041154 | 0.034107 | 0.426769 | 0.712358 | 0.018454 | -0.044513 | -0.007269 | 0.148600 | 0.006141 | -0.017982 | 0.064964 | -0.065446 | -0.010822 | 0.015069 | 0.033534 | -0.065925 | -0.021967 | -0.092654 | -0.057851 | -0.058985 | -0.080376 | -0.027351 | -0.034520 | 0.076224 | -0.004187 | 0.073659 | 0.020951 | 0.054936 | 0.079749 | -0.015689 | -0.010100 | -0.034609 | -0.028622 | 0.051986 | 0.056003 | -0.005580 | 0.031689 | 0.408428 | 0.094019 | 0.278556 | 0.274487 | 0.099336 | 0.099336 | 0.110548 | -0.226490 | 1.000000 | 0.029919 | 0.071426 | 0.060032 |
| Y001_4 | 0.034508 | 0.069050 | -0.014797 | 0.057092 | 0.033516 | -0.054987 | 0.025147 | 0.098700 | 0.032423 | 0.084733 | -0.009176 | 0.024442 | 0.001773 | 0.054244 | 0.078079 | 0.003514 | 0.057666 | -0.022925 | -0.097363 | 0.014082 | 0.103732 | 0.004875 | 0.028309 | -0.082988 | 0.042101 | -0.099095 | -0.061193 | 0.013850 | -0.012751 | -0.061261 | -0.007157 | 0.007364 | -0.017454 | 0.102075 | 0.169151 | -0.035013 | -0.131060 | 0.009722 | -0.021698 | 0.085339 | 0.129773 | 0.100342 | 0.038930 | 0.036189 | 0.102934 | 0.035460 | 0.184034 | 0.106826 | 0.107103 | 0.102102 | 0.065328 | -0.003983 | 0.060148 | 0.033704 | -0.021107 | -0.005213 | -0.046781 | 0.014926 | 0.030672 | -0.139607 | -0.001978 | -0.047063 | 0.022359 | 0.027700 | -0.001023 | -0.058826 | 0.135428 | 0.014000 | 0.060302 | -0.002001 | 0.023202 | 0.023713 | 0.066823 | 0.095480 | 0.076087 | 0.074710 | 0.057040 | 0.090255 | 0.071558 | 0.069534 | 0.019384 | 0.059768 | 0.079608 | 0.046849 | 0.054559 | 0.042225 | 0.121523 | 0.112648 | 0.027570 | 0.097928 | 0.053766 | 0.053766 | 0.051918 | 0.054580 | 0.056495 | 0.100965 | 0.092350 | 0.096878 | 0.105480 | 0.104437 | 0.065420 | 0.152712 | -0.128537 | -0.027347 | -0.031633 | 0.212100 | 0.191937 | 0.170006 | 0.176856 | 0.188089 | 0.155935 | 0.189856 | 0.215817 | 0.196417 | 0.179640 | 0.206456 | 0.159483 | 0.067427 | 0.160386 | 0.156386 | 0.002775 | -0.021895 | -0.163940 | 0.158923 | -0.012991 | 0.015031 | -0.027989 | 0.075202 | 0.069009 | 0.036529 | 0.141015 | 0.111467 | 0.189446 | 0.184367 | 0.164790 | 0.009961 | 0.194062 | -0.105061 | 0.198869 | -0.078258 | 0.020072 | 0.003412 | -0.092283 | 0.021449 | 0.065438 | -0.104665 | -0.097833 | 0.120076 | 0.131171 | 0.143013 | 0.054758 | 0.060745 | 0.042358 | 0.000494 | 0.002634 | -0.008193 | 0.031690 | -0.006142 | 0.026276 | -0.065328 | -0.042810 | -0.122840 | 0.093767 | 0.123994 | 0.102529 | -0.100149 | 0.100488 | 0.418647 | -0.016653 | 0.043769 | -0.000351 | 0.075148 | 0.170210 | 0.036202 | 0.031322 | -0.011137 | 0.070905 | 0.080621 | 0.023541 | 0.050840 | 0.039315 | -0.016960 | 0.013707 | 0.020597 | 0.011758 | -0.090763 | 0.006788 | -0.136448 | 0.114231 | 0.033819 | 0.096396 | 0.134784 | 0.114284 | 0.015144 | 0.029187 | 0.061798 | -0.047895 | -0.012851 | 0.009908 | 0.027001 | 0.038862 | -0.076613 | 0.114794 | 0.092652 | 0.027416 | 0.120848 | -0.114297 | 0.150813 | 0.117121 | 0.139366 | -0.087161 | 0.028618 | -0.092599 | -0.033162 | -0.091229 | -0.088549 | -0.150038 | -0.020041 | 0.108700 | 0.120997 | 0.033121 | 0.130822 | -0.006375 | -0.032234 | 0.029921 | -0.022805 | -0.043342 | -0.126484 | 0.007506 | -0.011199 | -0.140658 | -0.086730 | -0.064886 | -0.017050 | 0.084137 | 0.084137 | 0.084137 | 0.100387 | -0.063990 | 0.001629 | 0.139143 | 0.065642 | 0.000465 | 0.060983 | -0.151101 | 0.110916 | 0.064316 | 0.073361 | 0.042371 | -0.015965 | -0.004439 | -0.137599 | 0.092837 | -0.142789 | 0.092339 | 0.000637 | 0.121000 | -0.119063 | -0.181260 | 0.127041 | -0.129041 | -0.038127 | 0.088676 | -0.023344 | -0.016759 | 0.112280 | 0.136937 | -0.075259 | 0.098930 | 0.082626 | 0.078472 | 0.089600 | 0.006650 | -0.012053 | -0.006834 | 0.053302 | -0.053807 | -0.048174 | -0.059283 | 0.001543 | 0.073464 | 0.018229 | 0.046956 | -0.070653 | 0.168287 | 0.186729 | 0.185778 | 0.183033 | 0.055447 | 0.056765 | 0.047120 | 0.048712 | -0.032445 | 0.071619 | 0.001140 | 0.084131 | 0.000226 | -0.020137 | -0.001681 | -0.036643 | -0.080546 | 0.146302 | 0.070490 | -0.070905 | 0.070905 | -0.109988 | 0.052585 | 0.005171 | -0.060692 | -0.061695 | -0.030089 | -0.076550 | 0.055011 | 0.082130 | -0.063144 | -0.118282 | -0.034927 | 0.034732 | -0.045217 | -0.049092 | -0.050817 | -0.090998 | -0.062405 | -0.066303 | -0.074255 | -0.030055 | 0.022373 | 0.019735 | 0.025449 | 0.144322 | 0.014792 | 0.005510 | -0.103427 | 0.048366 | -0.093540 | -0.053909 | 0.149558 | -0.017539 | 0.032863 | 0.107090 | 0.090330 | 0.441540 | 0.112737 | -0.116778 | 0.142375 | 0.057430 | 0.021215 | -0.002545 | 0.033704 | 0.098930 | 0.102075 | 0.099334 | 0.001773 | 0.011758 | 0.013707 | 0.008960 | 0.057860 | 0.123373 | 0.099611 | 0.099382 | 0.095480 | 0.090255 | 0.071558 | 0.103424 | -0.078079 | 0.022925 | -0.082988 | -0.085991 | 0.085339 | -0.035013 | -0.131060 | -0.007407 | 0.015144 | 0.061798 | -0.047895 | 0.014804 | 0.132258 | 0.085703 | 0.151286 | 0.149358 | 0.058981 | 0.058981 | 0.069053 | 0.131468 | 0.029919 | 1.000000 | -0.133847 | 0.021107 |
| Y001_5 | 0.050213 | 0.051057 | 0.006980 | 0.060704 | 0.039279 | -0.110445 | 0.014154 | 0.088687 | -0.080449 | 0.033175 | 0.015593 | 0.084457 | 0.034484 | -0.008512 | -0.018393 | 0.042288 | 0.073328 | -0.075002 | -0.080648 | -0.018821 | -0.004480 | 0.061466 | -0.031232 | 0.028268 | 0.034340 | 0.087661 | -0.001612 | 0.218186 | 0.087273 | 0.076078 | 0.110971 | 0.161025 | 0.144545 | 0.004353 | 0.014973 | -0.000057 | -0.028249 | -0.060493 | 0.025817 | -0.080272 | -0.041181 | 0.050411 | 0.107519 | 0.004154 | 0.032271 | 0.073419 | -0.009143 | 0.019278 | 0.067165 | 0.140596 | 0.040134 | -0.093154 | -0.036839 | -0.083242 | 0.049152 | 0.003294 | -0.024027 | 0.031768 | -0.029391 | -0.030447 | -0.138858 | -0.129140 | -0.119680 | -0.090002 | -0.050497 | -0.121516 | 0.037051 | -0.005841 | -0.033017 | -0.064324 | -0.096023 | -0.100525 | -0.072666 | -0.144370 | -0.034035 | -0.000376 | 0.026928 | -0.116118 | 0.003221 | -0.041792 | 0.009485 | -0.010591 | -0.082528 | -0.045313 | -0.030670 | -0.022580 | -0.104529 | -0.100057 | 0.005333 | -0.076493 | 0.048023 | 0.048023 | -0.014030 | 0.012701 | 0.017460 | 0.015138 | 0.004997 | 0.003240 | 0.013370 | 0.016667 | -0.061898 | 0.019205 | 0.127795 | -0.008391 | -0.070941 | -0.048938 | -0.042230 | -0.045050 | -0.085759 | -0.093648 | -0.100818 | -0.098137 | -0.075000 | -0.087485 | -0.077256 | -0.062826 | -0.091358 | -0.115994 | 0.041770 | 0.140833 | 0.095384 | 0.001872 | 0.053208 | -0.022044 | 0.070185 | 0.027529 | 0.057145 | -0.024462 | -0.048477 | 0.072199 | -0.022625 | 0.001738 | 0.073835 | 0.121512 | -0.024224 | -0.171230 | -0.053767 | -0.073615 | -0.022852 | -0.209755 | -0.129468 | -0.148065 | -0.077990 | 0.095491 | -0.081894 | -0.244132 | -0.179793 | -0.083072 | -0.126765 | -0.067329 | -0.027348 | 0.037692 | -0.041719 | -0.033413 | -0.096565 | -0.027805 | -0.057522 | 0.079249 | 0.044529 | -0.029335 | 0.030011 | 0.059419 | 0.064059 | -0.074181 | 0.091261 | 0.018292 | -0.052559 | -0.159964 | -0.185876 | -0.030329 | 0.025594 | 0.105012 | 0.016229 | 0.039408 | -0.152310 | -0.132401 | 0.038006 | 0.011962 | 0.019061 | 0.002446 | 0.027975 | 0.033452 | -0.117822 | 0.027623 | 0.025111 | 0.007386 | -0.015321 | 0.093004 | -0.054771 | -0.125825 | -0.037021 | -0.028121 | -0.011250 | -0.004664 | -0.005325 | 0.027434 | -0.073482 | 0.054113 | -0.024573 | 0.038803 | -0.030200 | -0.100305 | -0.025711 | -0.027886 | 0.005123 | 0.004592 | -0.111880 | 0.112129 | -0.027374 | 0.003069 | -0.063756 | -0.140156 | -0.078109 | -0.050873 | -0.096903 | -0.186522 | -0.078723 | -0.164885 | -0.110163 | -0.155358 | -0.064071 | -0.040731 | -0.088688 | -0.094912 | -0.163952 | -0.123988 | -0.234676 | -0.104933 | -0.114854 | -0.085634 | -0.126952 | -0.194751 | 0.017124 | -0.006225 | 0.029148 | 0.029148 | 0.029148 | -0.006212 | -0.165202 | -0.112630 | -0.057337 | 0.054221 | -0.020606 | -0.070536 | -0.141710 | 0.012786 | -0.090821 | -0.029628 | -0.039820 | -0.130140 | -0.067614 | 0.055265 | -0.043444 | -0.071464 | -0.002792 | -0.077999 | 0.035681 | 0.010512 | -0.038570 | -0.037781 | 0.090125 | 0.002283 | 0.069321 | -0.046052 | -0.107334 | -0.007099 | -0.027832 | -0.005870 | -0.056171 | 0.030167 | 0.054739 | 0.103838 | 0.156804 | 0.134344 | -0.010723 | 0.016510 | -0.015898 | -0.023512 | -0.021796 | 0.003470 | 0.007949 | 0.057649 | -0.010714 | -0.012688 | -0.015578 | -0.027568 | -0.028968 | -0.000821 | 0.043213 | 0.011277 | 0.025391 | 0.023832 | -0.057775 | -0.071666 | 0.102017 | -0.006793 | -0.018050 | 0.086864 | -0.003717 | -0.040152 | -0.088569 | -0.015872 | 0.010226 | -0.038006 | 0.038006 | 0.060411 | 0.018289 | 0.047693 | 0.026227 | -0.020474 | 0.050132 | 0.026207 | -0.001335 | 0.015419 | 0.014197 | 0.048693 | 0.039906 | 0.075697 | 0.022034 | -0.001801 | -0.053188 | 0.001315 | 0.000758 | -0.042557 | -0.022558 | -0.077851 | -0.087605 | 0.002994 | 0.009183 | -0.054874 | 0.007470 | 0.115186 | 0.033879 | 0.029796 | 0.002816 | 0.066941 | 0.015521 | -0.062323 | 0.094161 | 0.048208 | 0.039398 | 0.510287 | 0.092670 | 0.041361 | -0.090747 | -0.058880 | 0.033555 | -0.073462 | -0.083242 | -0.056171 | 0.004353 | -0.074812 | 0.034484 | 0.025111 | -0.117822 | -0.044825 | -0.074576 | -0.020118 | 0.004114 | -0.043403 | -0.144370 | -0.116118 | 0.003221 | -0.110067 | 0.018393 | 0.075002 | 0.028268 | 0.055699 | -0.080272 | -0.000057 | -0.028249 | -0.058791 | -0.004664 | 0.027434 | -0.073482 | -0.051194 | 0.086353 | 0.173891 | 0.189131 | 0.181195 | 0.050613 | 0.050613 | 0.201818 | 0.047120 | 0.071426 | -0.133847 | 1.000000 | -0.049152 |
| inverse_Q46 | 0.014024 | 0.062452 | 0.014992 | 0.096979 | 0.058092 | -0.095931 | -0.048116 | -0.159143 | -0.009166 | -0.098175 | -0.006396 | -0.110458 | -0.100021 | -0.081338 | 0.088650 | -0.054504 | -0.006127 | 0.037876 | -0.104678 | 0.098798 | 0.012205 | -0.032973 | 0.031293 | -0.027708 | -0.115408 | -0.035484 | -0.119962 | 0.088275 | -0.084350 | -0.004251 | -0.125888 | -0.107315 | -0.025515 | -0.183220 | -0.027955 | 0.010701 | -0.008951 | 0.007038 | -0.051793 | -0.091603 | -0.094733 | -0.109046 | -0.099815 | -0.056819 | 0.003923 | -0.052690 | -0.090227 | -0.099327 | -0.047731 | 0.000789 | 0.090330 | -0.051395 | -0.124204 | -0.120704 | -1.000000 | -0.506641 | 0.394343 | 0.452586 | 0.437768 | 0.231129 | 0.183272 | 0.247042 | 0.270797 | 0.254855 | -0.178191 | 0.044243 | -0.137760 | -0.035540 | -0.047881 | -0.015329 | 0.037204 | 0.029844 | -0.103714 | -0.114536 | -0.121256 | -0.180421 | -0.243370 | -0.232350 | -0.270218 | -0.257286 | -0.237262 | -0.208465 | -0.211129 | -0.215194 | -0.196201 | -0.193136 | -0.192700 | -0.168162 | -0.162786 | -0.178915 | -0.254598 | -0.254598 | -0.157023 | -0.208507 | -0.131726 | -0.141758 | -0.108001 | -0.127248 | -0.159950 | -0.159749 | -0.080765 | 0.085346 | -0.181671 | -0.088351 | 0.213212 | 0.159728 | 0.162999 | 0.165941 | 0.175990 | 0.150442 | 0.162634 | 0.191268 | 0.162640 | 0.152556 | 0.191768 | 0.174159 | 0.060624 | -0.023760 | 0.085727 | 0.069182 | -0.043823 | -0.072357 | -0.072599 | -0.013600 | -0.163120 | -0.133605 | -0.149615 | -0.081816 | 0.015255 | -0.085785 | 0.043968 | 0.119652 | 0.100123 | 0.100021 | 0.014936 | -0.030363 | -0.068993 | -0.095306 | 0.089246 | 0.014142 | -0.007740 | -0.034804 | -0.244736 | 0.079449 | 0.088937 | -0.037193 | -0.025467 | 0.101966 | 0.075410 | 0.148830 | 0.184983 | 0.106245 | 0.125834 | 0.085045 | 0.017994 | 0.077589 | 0.070525 | -0.018331 | 0.027789 | 0.011589 | 0.046322 | -0.001062 | 0.050459 | 0.079597 | 0.010310 | -0.056850 | 0.010583 | 0.021225 | -0.013845 | 0.014479 | 0.049011 | -0.024942 | -0.022126 | -0.045399 | 0.127793 | 0.051494 | -0.104830 | -0.024766 | -0.086139 | -0.101104 | -0.018608 | -0.050666 | -0.123326 | -0.249817 | -0.051300 | -0.108654 | -0.063817 | 0.010085 | 0.157914 | 0.100443 | 0.158784 | 0.165261 | 0.173135 | 0.107073 | 0.089848 | 0.122731 | 0.078026 | 0.049802 | 0.041711 | 0.114761 | 0.071713 | 0.107160 | 0.095730 | 0.118097 | 0.060079 | 0.109843 | 0.045462 | 0.030511 | 0.065211 | 0.055923 | -0.106264 | -0.052481 | 0.014434 | -0.114474 | 0.008317 | -0.071121 | -0.025401 | -0.084789 | -0.011783 | -0.046816 | 0.102219 | 0.135313 | 0.065140 | 0.093234 | -0.008263 | 0.022737 | 0.067534 | 0.024737 | 0.053420 | 0.145039 | 0.056314 | 0.051497 | -0.087746 | -0.050565 | 0.032178 | 0.032178 | 0.032178 | -0.105752 | -0.091330 | 0.005807 | 0.030534 | -0.012749 | -0.093698 | 0.060803 | 0.006232 | -0.020109 | -0.136754 | 0.009690 | -0.072711 | -0.100927 | 0.008263 | -0.116761 | 0.040610 | 0.062022 | 0.062394 | -0.083867 | 0.077211 | -0.049650 | -0.033015 | 0.123466 | -0.076049 | -0.065433 | -0.035668 | -0.075386 | -0.062635 | 0.217538 | 0.211310 | -0.200452 | -0.157584 | -0.113625 | -0.101864 | -0.117548 | -0.148084 | -0.154017 | -0.007892 | 0.009988 | -0.009220 | 0.005827 | -0.010803 | -0.041248 | 0.013486 | 0.008016 | -0.049639 | -0.008400 | 0.010114 | 0.008855 | 0.062732 | 0.080278 | -0.014954 | -0.008787 | -0.048202 | -0.058225 | -0.011242 | 0.045207 | -0.011875 | -0.023238 | 0.039029 | 0.045872 | -0.011174 | -0.160974 | -0.159930 | 0.181902 | 0.141091 | 0.104830 | -0.104830 | -0.186400 | -0.024012 | -0.024179 | -0.162109 | -0.004253 | -0.066400 | -0.236504 | -0.033812 | 0.024783 | -0.151453 | -0.085806 | -0.136667 | 0.024074 | 0.097765 | 0.117778 | 0.011317 | 0.044963 | 0.021078 | -0.199087 | -0.166550 | 0.094412 | 0.068449 | -0.086511 | -0.061913 | 0.148449 | -0.066853 | -0.066540 | -0.101730 | 0.100793 | -0.146378 | -0.159326 | 0.173989 | 0.073093 | 0.182750 | 0.077746 | 0.080116 | 0.016728 | 0.035161 | -0.068548 | -0.079057 | -0.183210 | 0.013232 | 0.066722 | -0.120704 | -0.157584 | -0.183220 | -0.206931 | -0.100021 | -0.051300 | -0.123326 | -0.118664 | 0.093862 | 0.156234 | 0.155969 | 0.149807 | -0.114536 | -0.232350 | -0.270218 | -0.243467 | -0.088650 | -0.037876 | -0.027708 | -0.081595 | -0.091603 | 0.010701 | -0.008951 | -0.033973 | 0.107073 | 0.122731 | 0.078026 | 0.128965 | 0.012887 | 0.031659 | 0.042118 | 0.035144 | 0.007938 | 0.007938 | 0.028458 | -0.035201 | 0.060032 | 0.021107 | -0.049152 | 1.000000 |
388 rows × 388 columns
fig = px.imshow(correlation_matrix_df,
text_auto=True)
fig
#pio.write_html(fig, file='plot.html', auto_open=True) #Save and open the plot in a browser
corrQ46 = correlation_matrix_df.loc['inverse_Q46'].dropna().reset_index()
corrQ50 = correlation_matrix_df.loc['Q50'].dropna().reset_index()
corrQ46[corrQ46['inverse_Q46'] >= 0.2].sort_values('inverse_Q46', ascending=False)
| index | inverse_Q46 | |
|---|---|---|
| 387 | inverse_Q46 | 1.000000 |
| 57 | Q49 | 0.452586 |
| 58 | Q50 | 0.437768 |
| 56 | Q48 | 0.394343 |
| 62 | Q54 | 0.270797 |
| 63 | Q55 | 0.254855 |
| 61 | Q53 | 0.247042 |
| 59 | Q51 | 0.231129 |
| 265 | Q251 | 0.217538 |
| 104 | Q93 | 0.213212 |
| 266 | Q252 | 0.211310 |
corrQ50[corrQ50['Q50'] >= 0.2].sort_values('Q50', ascending=False)
| index | Q50 | |
|---|---|---|
| 58 | Q50 | 1.000000 |
| 57 | Q49 | 0.667909 |
| 56 | Q48 | 0.495239 |
| 62 | Q54 | 0.438604 |
| 387 | inverse_Q46 | 0.437768 |
| 61 | Q53 | 0.377738 |
| 59 | Q51 | 0.313080 |
| 60 | Q52 | 0.275484 |
| 63 | Q55 | 0.259059 |
| 153 | Q142 | 0.240693 |
| 301 | Q288 | 0.227805 |
| 150 | Q139 | 0.216577 |
| 148 | Q137 | 0.212883 |
| 221 | Q210 | 0.204108 |
variable_view[variable_view['Column'] == 'Q48'].values
array([['Q48', 'How much freedom of choice and control',
{-5.0: 'Other missing; Multiple answers Mail (EVS)', -4.0: 'Not asked', -2.0: 'No answer', -1.0: "Don't know", 1.0: 'None at all', 2.0: '2', 3.0: '3', 4.0: '4', 5.0: '5', 6.0: '6', 7.0: '7', 8.0: '8', 9.0: '9', 10.0: 'A great deal'}]],
dtype=object)
Now it regression time¶
y = DataFrame['Feeling of happiness (NV_Q46)_inverse']
X = DataFrame[['Satisfaction with financial situation of household (NV_Q50)','Highest educational level: Respondent [ISCED 2011] (NV_Q275)']]
X = sm.add_constant(X)
model = sm.OLS(y, X).fit()
print(model.summary())
OLS Regression Results
=================================================================================================
Dep. Variable: Feeling of happiness (NV_Q46)_inverse R-squared: 0.193
Model: OLS Adj. R-squared: 0.192
Method: Least Squares F-statistic: 143.4
Date: Wed, 12 Feb 2025 Prob (F-statistic): 1.44e-56
Time: 23:27:39 Log-Likelihood: -890.39
No. Observations: 1200 AIC: 1787.
Df Residuals: 1197 BIC: 1802.
Df Model: 2
Covariance Type: nonrobust
================================================================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------------------------------------------------------
const 2.1882 0.050 44.038 0.000 2.091 2.286
Satisfaction with financial situation of household (NV_Q50) 0.1177 0.007 16.936 0.000 0.104 0.131
Highest educational level: Respondent [ISCED 2011] (NV_Q275) 0.0083 0.007 1.224 0.221 -0.005 0.022
==============================================================================
Omnibus: 14.789 Durbin-Watson: 1.764
Prob(Omnibus): 0.001 Jarque-Bera (JB): 22.964
Skew: -0.071 Prob(JB): 1.03e-05
Kurtosis: 3.663 Cond. No. 24.2
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
Conclusion from Regression Analysis¶
The Ordinary Least Squares (OLS) regression results provide insights into the relationship between happiness and two independent variables: satisfaction with financial situation and education level.
Key Findings:¶
Model Fit:
- The model's R-squared is 0.193, meaning about 19.3% of the variation in happiness is explained by the independent variables.
- The Adjusted R-squared is also 0.192, indicating a consistent fit even after adjusting for the number of predictors.
Significance of Predictors:
- Satisfaction with financial situation:
- Coefficient: 0.1177
- p-value: 0.000 (< 0.05)
- This variable has a significant positive relationship with happiness, implying that as financial satisfaction increases, the happiness score increases.
- Education level:
- Coefficient: 0.0083
- p-value: 0.221 (> 0.05)
- This variable is not statistically significant in predicting happiness.
- Satisfaction with financial situation:
Intercept:
- The constant is 2.1882, representing the predicted happiness when both predictors are zero.
Model Performance:
- The F-statistic (143.4, p-value = 1.44e-56) indicates that the overall model is statistically significant.
- However, the relatively low R-squared suggests that there are other factors affecting happiness that are not captured by this model.
Assumptions Check:
- Durbin-Watson statistic (1.764) suggests mild autocorrelation, but it is within an acceptable range.
- The Omnibus test (p-value = 0.001) and Jarque-Bera test (p-value = 1.03e-05) suggest some deviation from normality in residuals.
Conclusion:¶
The regression analysis shows that financial satisfaction is a significant predictor of happiness, while education level does not have a statistically significant effect. The low R-squared value implies that this model could be improved by including more explanatory variables.
# Predecting y values
DataFrame['y_pred'] = model.predict(X)
# Create grid for regression plane
x1_range = np.linspace(DataFrame['Satisfaction with financial situation of household (NV_Q50)'].min(),
DataFrame['Satisfaction with financial situation of household (NV_Q50)'].max(), 10)
x2_range = np.linspace(DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'].min(),
DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'].max(), 10)
x1_grid, x2_grid = np.meshgrid(x1_range, x2_range)
x1_flat = x1_grid.flatten()
x2_flat = x2_grid.flatten()
# Prepare the grid for prediction
grid_data = pd.DataFrame({
'const': 1,
'Satisfaction with financial situation of household (NV_Q50)': x1_flat,
'Highest educational level: Respondent [ISCED 2011] (NV_Q275)': x2_flat
})
# Predict values for the regression plane
y_grid = model.predict(grid_data)
y_grid = y_grid.values.reshape(x1_grid.shape)
# Visualize using Plotly
fig = go.Figure()
# Add actual data points
fig.add_trace(go.Scatter3d(
x=DataFrame['Satisfaction with financial situation of household (NV_Q50)'],
y=DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'],
z=DataFrame['Feeling of happiness (NV_Q46)_inverse'],
mode='markers',
marker=dict(size=5, color='blue'),
name='Actual',
hovertemplate=(
'Satisfaction: %{x}<br>'
'Education Level: %{y}<br>'
'Happiness: %{z}<br>'
)
))
# Add predicted data points
fig.add_trace(go.Scatter3d(
x=DataFrame['Satisfaction with financial situation of household (NV_Q50)'],
y=DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'],
z=DataFrame['y_pred'],
mode='markers',
marker=dict(size=5, color='red'),
name='Predicted',
hovertemplate=(
'Satisfaction: %{x}<br>'
'Education Level: %{y}<br>'
'Predicted Happiness: %{z}<br>'
)
))
# Add regression plane
fig.add_trace(go.Surface(
x=x1_grid,
y=x2_grid,
z=y_grid,
colorscale='Viridis',
opacity=0.5,
name='Regression Plane',
hovertemplate=(
'Satisfaction: %{x}<br>'
'Education Level: %{y}<br>'
'Plane Value: %{z}<br>'
)
))
# Customize layout to show all values on the axis
fig.update_layout(
scene=dict(
xaxis=dict(
title='Satisfaction with financial situation',
range=[DataFrame['Satisfaction with financial situation of household (NV_Q50)'].min(),
DataFrame['Satisfaction with financial situation of household (NV_Q50)'].max()],
tickangle=-30 # Rotate ticks slightly for better readability
),
yaxis=dict(
title='Education Level (ISCED)',
range=[DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'].min(),
DataFrame['Highest educational level: Respondent [ISCED 2011] (NV_Q275)'].max()],
tickangle=-30 # Rotate ticks slightly
),
zaxis=dict(
title='Happiness',
range=[DataFrame['Feeling of happiness (NV_Q46)_inverse'].min(),
DataFrame['Feeling of happiness (NV_Q46)_inverse'].max()]
)
),
title='Multilinear Regression',
margin=dict(l=50, r=50, b=50, t=50), # Adjust margins for cleaner layout
legend=dict(
orientation="h", # Horizontal legend layout
yanchor="bottom", # Anchor to the bottom of the plot
y=-0.2, # Position slightly below the plot
xanchor="center", # Center the legend
x=0.5 # Align horizontally at the center
)
)
fig.show()
#pio.write_html(fig, file='plot.html', auto_open=True)
From the regression plot, we can observe the following:
Regression Plane:
- The multivariate regression plane fits well within the data points, showing the relationship between the independent variables (
Satisfaction with financial situationandEducation Level) and the dependent variable (Happiness).
- The multivariate regression plane fits well within the data points, showing the relationship between the independent variables (
Actual vs. Predicted Values:
- The blue points represent the actual data, while the red points are the predicted values from the regression model.
- Most predicted points align closely with the actual values, indicating that the model captures the general trend of the data well.
Insights:
- Higher satisfaction with the financial situation appears correlated with increased happiness (lower inverse happiness).
- Education level seems to influence happiness to a lesser extent in comparison, as the changes along this axis are less pronounced.
Model Fit:
- The plot suggests a decent model fit with minor deviations, particularly in some regions where the actual points deviate from the regression plane.
Conclusion:¶
The regression model demonstrates a strong relationship between financial satisfaction and happiness, with education level having a more subtle impact. Future work could involve validating the model with statistical metrics (e.g., R-squared, RMSE) and exploring interaction effects or additional variables for improved insights.
DataFrame['How much freedom of choice and control (Q48)'] = df['Q48']
y = DataFrame['Feeling of happiness (NV_Q46)_inverse']
X = DataFrame[['Satisfaction with financial situation of household (NV_Q50)','How much freedom of choice and control (Q48)']]
X = sm.add_constant(X)
model = sm.OLS(y, X).fit()
print(model.summary())
OLS Regression Results
=================================================================================================
Dep. Variable: Feeling of happiness (NV_Q46)_inverse R-squared: 0.247
Model: OLS Adj. R-squared: 0.246
Method: Least Squares F-statistic: 196.7
Date: Wed, 12 Feb 2025 Prob (F-statistic): 1.33e-74
Time: 23:27:39 Log-Likelihood: -848.76
No. Observations: 1200 AIC: 1704.
Df Residuals: 1197 BIC: 1719.
Df Model: 2
Covariance Type: nonrobust
===============================================================================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------------------------------------------------------
const 1.9535 0.052 37.509 0.000 1.851 2.056
Satisfaction with financial situation of household (NV_Q50) 0.0813 0.008 10.526 0.000 0.066 0.096
How much freedom of choice and control (Q48) 0.0708 0.008 9.360 0.000 0.056 0.086
==============================================================================
Omnibus: 16.545 Durbin-Watson: 1.777
Prob(Omnibus): 0.000 Jarque-Bera (JB): 27.717
Skew: 0.039 Prob(JB): 9.58e-07
Kurtosis: 3.740 Cond. No. 35.6
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
# Predecting y values
DataFrame['y_pred'] = model.predict(X)
# Create grid for regression plane
x1_range = np.linspace(DataFrame['Satisfaction with financial situation of household (NV_Q50)'].min(),
DataFrame['Satisfaction with financial situation of household (NV_Q50)'].max(), 10)
x2_range = np.linspace(DataFrame['How much freedom of choice and control (Q48)'].min(),
DataFrame['How much freedom of choice and control (Q48)'].max(), 10)
x1_grid, x2_grid = np.meshgrid(x1_range, x2_range)
x1_flat = x1_grid.flatten()
x2_flat = x2_grid.flatten()
# Prepare the grid for prediction
grid_data = pd.DataFrame({
'const': 1,
'Satisfaction with financial situation of household (NV_Q50)': x1_flat,
'How much freedom of choice and control': x2_flat
})
# Predict values for the regression plane
y_grid = model.predict(grid_data)
y_grid = y_grid.values.reshape(x1_grid.shape)
# Visualize using Plotly
fig = go.Figure()
# Add actual data points
fig.add_trace(go.Scatter3d(
x=DataFrame['Satisfaction with financial situation of household (NV_Q50)'],
y=DataFrame['How much freedom of choice and control (Q48)'],
z=DataFrame['Feeling of happiness (NV_Q46)_inverse'],
mode='markers',
marker=dict(size=5, color='blue'),
name='Actual',
hovertemplate=(
'Satisfaction: %{x}<br>'
'freedom of choice and control: %{y}<br>'
'Happiness: %{z}<br>'
)
))
# Add predicted data points
fig.add_trace(go.Scatter3d(
x=DataFrame['Satisfaction with financial situation of household (NV_Q50)'],
y=DataFrame['How much freedom of choice and control (Q48)'],
z=DataFrame['y_pred'],
mode='markers',
marker=dict(size=5, color='red'),
name='Predicted',
hovertemplate=(
'Satisfaction: %{x}<br>'
'freedom of choice and control: %{y}<br>'
'Predicted Happiness: %{z}<br>'
)
))
# Add regression plane
fig.add_trace(go.Surface(
x=x1_grid,
y=x2_grid,
z=y_grid,
colorscale='Viridis',
opacity=0.5,
name='Regression Plane',
hovertemplate=(
'Satisfaction: %{x}<br>'
'freedom of choice and control: %{y}<br>'
'Plane Value: %{z}<br>'
)
))
# Customize layout to show all values on the axis
fig.update_layout(
scene=dict(
xaxis=dict(
title='Satisfaction with financial situation',
range=[DataFrame['Satisfaction with financial situation of household (NV_Q50)'].min(),
DataFrame['Satisfaction with financial situation of household (NV_Q50)'].max()],
tickangle=-30 # Rotate ticks slightly for better readability
),
yaxis=dict(
title='freedom of choice and control',
range=[DataFrame['How much freedom of choice and control (Q48)'].min(),
DataFrame['How much freedom of choice and control (Q48)'].max()],
tickangle=-30 # Rotate ticks slightly
),
zaxis=dict(
title='Happiness',
range=[DataFrame['Feeling of happiness (NV_Q46)_inverse'].min(),
DataFrame['Feeling of happiness (NV_Q46)_inverse'].max()]
)
),
title='Multilinear Regression',
margin=dict(l=50, r=50, b=50, t=50), # Adjust margins for cleaner layout
legend=dict(
orientation="h", # Horizontal legend layout
yanchor="bottom", # Anchor to the bottom of the plot
y=-0.2, # Position slightly below the plot
xanchor="center", # Center the legend
x=0.5 # Align horizontally at the center
)
)
fig.show()
#pio.write_html(fig, file='plot.html', auto_open=True)
# setupping the map
morocco_data = gpd.read_file("ma.json")
morocco_data.drop(columns='source',inplace=True)
# if the file was in goejson fiormat u need to load t as json format
# morocco_geojson = json.loads(morocco_data.to_json())
morocco_data
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) File ~\AppData\Roaming\Python\Python312\site-packages\IPython\core\formatters.py:343, in BaseFormatter.__call__(self, obj) 341 pass 342 else: --> 343 return printer(obj) 344 # Finally look for special method names 345 method = get_real_method(obj, self.print_method) Cell In[1], line 227, in __DW_OUTPUT_FORMATTER__.<locals>.DataWrangler.formatter(cls, var, **kwargs) 225 @classmethod 226 def formatter(cls, var, **kwargs): --> 227 s = cls(var, **kwargs) 228 return s._repr_dw_() Cell In[1], line 221, in __DW_OUTPUT_FORMATTER__.<locals>.DataWrangler.__init__(self, expr_val) 218 self.id = str(uuid.uuid4()) 219 pandas_df, conversion_method = api["pandas_transport"]["convert_to_pandas"](expr_val) 220 tmp_vars[self.id] = { --> 221 "converted": pandas_df.copy(deep=False), # create a shallow copy in case a displayed object is mutated in the same cell 222 "conversion_method": conversion_method 223 } AttributeError: 'NoneType' object has no attribute 'copy'
| id | name | geometry | |
|---|---|---|---|
| 0 | MA11 | Laâyoune-Sakia El Hamra | POLYGON ((-8.75659 27.14772, -8.75422 27.14715... |
| 1 | MA01 | Tangier-Tetouan-Al Hoceima | POLYGON ((-3.82389 35.19936, -3.82346 35.19887... |
| 2 | MA02 | Oriental | POLYGON ((-3.82012 34.88753, -3.81998 34.89199... |
| 3 | MA08 | Drâa-Tafilalet | POLYGON ((-4.01816 32.60844, -4.00295 32.57946... |
| 4 | MA09 | Souss-Massa | POLYGON ((-7.73179 31.13463, -7.73274 31.13365... |
| 5 | MA10 | Guelmim-Oued Noun | POLYGON ((-8.75659 27.14772, -8.80915 27.16043... |
| 6 | MA06 | Casablanca-Settat | POLYGON ((-9.05932 32.72101, -9.05904 32.7215,... |
| 7 | MA07 | Marrakech-Safi | POLYGON ((-7.18452 31.42896, -7.1929 31.42544,... |
| 8 | MA12 | Dakhla-Oued Ed-Dahab | POLYGON ((-14.90488 24.6832, -14.89861 24.6739... |
| 9 | MA04 | Rabat-Salé-Kenitra | POLYGON ((-6.2434 35.00139, -6.23397 34.99872,... |
| 10 | MA03 | Fez-Meknes | POLYGON ((-5.31515 34.5159, -5.28082 34.51478,... |
| 11 | MA05 | Béni Mellal-Khénifra | POLYGON ((-5.25455 32.86937, -5.25284 32.86344... |
labeled_df['N_REGION_ISO']
0 MA-09 Souss-Massa
1 MA-09 Souss-Massa
2 MA-09 Souss-Massa
3 MA-09 Souss-Massa
4 MA-09 Souss-Massa
...
1195 MA-02 L'Oriental
1196 MA-02 L'Oriental
1197 MA-02 L'Oriental
1198 MA-02 L'Oriental
1199 MA-02 L'Oriental
Name: N_REGION_ISO, Length: 1200, dtype: object
# functoin to seperate the region id from its name
id_list = []
regions_name = []
y = labeled_df['N_REGION_ISO'].str.split(' ')
for x in y:
id_list.append(x[0])
regions_name.append(x[1])
# creating the dataframe the will be ploted 'chromaplot_df'
chromaplot_df = pd.DataFrame({
'Satisfaction with financial situation of household (NV_Q50)': df['Q50'],
'Feeling of happiness (NV_Q46)': df['Q46'],
'Feeling of happiness (NV_Q46)_reversed': DataFrame['Feeling of happiness (NV_Q46)_inverse'],
'Feeling of happiness (LV_Q46)': labeled_df['Q46'],
'N_REGION_ISO': labeled_df['N_REGION_ISO'],
})
chromaplot_df['ID_list'] = id_list
chromaplot_df['ID_list']= chromaplot_df['ID_list'].str.replace('-', '')
chromaplot_df['regions_name'] = regions_name
chromaplot_df
| Satisfaction with financial situation of household (NV_Q50) | Feeling of happiness (NV_Q46) | Feeling of happiness (NV_Q46)_reversed | Feeling of happiness (LV_Q46) | N_REGION_ISO | ID_list | regions_name | |
|---|---|---|---|---|---|---|---|
| 0 | 6.0 | 2.0 | 3 | Quite happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| 1 | 2.0 | 4.0 | 1 | Not at all happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| 2 | 3.0 | 3.0 | 2 | Not very happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| 3 | 6.0 | 2.0 | 3 | Quite happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| 4 | 4.0 | 3.0 | 2 | Not very happy | MA-09 Souss-Massa | MA09 | Souss-Massa |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 1195 | 6.0 | 1.0 | 4 | Very happy | MA-02 L'Oriental | MA02 | L'Oriental |
| 1196 | 6.0 | 2.0 | 3 | Quite happy | MA-02 L'Oriental | MA02 | L'Oriental |
| 1197 | 3.0 | 2.0 | 3 | Quite happy | MA-02 L'Oriental | MA02 | L'Oriental |
| 1198 | 3.0 | 2.0 | 3 | Quite happy | MA-02 L'Oriental | MA02 | L'Oriental |
| 1199 | 7.0 | 2.0 | 3 | Quite happy | MA-02 L'Oriental | MA02 | L'Oriental |
1200 rows × 7 columns
# calculating the mean of Feeling of happiness for each region
aggregated_data_FoH = chromaplot_df.groupby(['ID_list','N_REGION_ISO'])['Feeling of happiness (NV_Q46)_reversed'].mean().reset_index()
aggregated_data_FoH
| ID_list | N_REGION_ISO | Feeling of happiness (NV_Q46)_reversed | |
|---|---|---|---|
| 0 | MA01 | MA-01 Tanger-Tetouan-Al Hoceima | 2.941667 |
| 1 | MA02 | MA-02 L'Oriental | 3.137500 |
| 2 | MA03 | MA-03 Fes-Meknes | 3.020000 |
| 3 | MA04 | MA-04 Rabat-Sale-Kenitra | 2.911765 |
| 4 | MA05 | MA-05 Beni Mellal-Khenifra | 2.812500 |
| 5 | MA06 | MA-06 Casablanca-Settat | 2.934615 |
| 6 | MA07 | MA-07 Marrakech-Safi | 2.893333 |
| 7 | MA08 | MA-08 Draa-Tafilalet | 2.980000 |
| 8 | MA09 | MA-09 Souss-Massa | 2.940000 |
| 9 | MA10 | MA-10 Guelmim-Oued Noun | 2.900000 |
| 10 | MA11 | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 2.800000 |
| 11 | MA12 | MA-12 Dakhla-Oued Ed-Dahab (EH) | 3.000000 |
# calculating the mean of Satisfaction with financial situation of household for each region
aggregated_data_SFHS = chromaplot_df.groupby(['ID_list','N_REGION_ISO'])['Satisfaction with financial situation of household (NV_Q50)'].mean().reset_index()
aggregated_data_SFHS
| ID_list | N_REGION_ISO | Satisfaction with financial situation of household (NV_Q50) | |
|---|---|---|---|
| 0 | MA01 | MA-01 Tanger-Tetouan-Al Hoceima | 6.508333 |
| 1 | MA02 | MA-02 L'Oriental | 6.262500 |
| 2 | MA03 | MA-03 Fes-Meknes | 6.766667 |
| 3 | MA04 | MA-04 Rabat-Sale-Kenitra | 6.141176 |
| 4 | MA05 | MA-05 Beni Mellal-Khenifra | 5.975000 |
| 5 | MA06 | MA-06 Casablanca-Settat | 6.080769 |
| 6 | MA07 | MA-07 Marrakech-Safi | 6.033333 |
| 7 | MA08 | MA-08 Draa-Tafilalet | 6.340000 |
| 8 | MA09 | MA-09 Souss-Massa | 6.110000 |
| 9 | MA10 | MA-10 Guelmim-Oued Noun | 6.200000 |
| 10 | MA11 | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 6.850000 |
| 11 | MA12 | MA-12 Dakhla-Oued Ed-Dahab (EH) | 5.400000 |
# updating the chromaplot_df
chromaplot_df = chromaplot_df.merge(aggregated_data_FoH, how='left', on='ID_list')
chromaplot_df = chromaplot_df.merge(aggregated_data_SFHS, how='left', on='ID_list')
chromaplot_df.rename(columns={
'Feeling of happiness (NV_Q46)_reversed_x': 'Feeling of happiness (NV_Q46)_reversed',
'Feeling of happiness (NV_Q46)_reversed_y': 'mean_FoH',
'Satisfaction with financial situation of household (NV_Q50)_x':'Satisfaction with financial situation of household (NV_Q50)',
'Satisfaction with financial situation of household (NV_Q50)_y':'mean_SFSH'
},
inplace=True)
chromaplot_df
# re-ordering the placement of the columns
reorder=['ID_list','regions_name','N_REGION_ISO','Feeling of happiness (LV_Q46)','Feeling of happiness (NV_Q46)','Feeling of happiness (NV_Q46)_reversed','mean_FoH','Satisfaction with financial situation of household (NV_Q50)','mean_SFSH']
chromaplot_df = chromaplot_df[reorder]
chromaplot_df
| ID_list | regions_name | N_REGION_ISO | Feeling of happiness (LV_Q46) | Feeling of happiness (NV_Q46) | Feeling of happiness (NV_Q46)_reversed | mean_FoH | Satisfaction with financial situation of household (NV_Q50) | mean_SFSH | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | MA09 | Souss-Massa | MA-09 Souss-Massa | Quite happy | 2.0 | 3 | 2.9400 | 6.0 | 6.1100 |
| 1 | MA09 | Souss-Massa | MA-09 Souss-Massa | Not at all happy | 4.0 | 1 | 2.9400 | 2.0 | 6.1100 |
| 2 | MA09 | Souss-Massa | MA-09 Souss-Massa | Not very happy | 3.0 | 2 | 2.9400 | 3.0 | 6.1100 |
| 3 | MA09 | Souss-Massa | MA-09 Souss-Massa | Quite happy | 2.0 | 3 | 2.9400 | 6.0 | 6.1100 |
| 4 | MA09 | Souss-Massa | MA-09 Souss-Massa | Not very happy | 3.0 | 2 | 2.9400 | 4.0 | 6.1100 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1195 | MA02 | L'Oriental | MA-02 L'Oriental | Very happy | 1.0 | 4 | 3.1375 | 6.0 | 6.2625 |
| 1196 | MA02 | L'Oriental | MA-02 L'Oriental | Quite happy | 2.0 | 3 | 3.1375 | 6.0 | 6.2625 |
| 1197 | MA02 | L'Oriental | MA-02 L'Oriental | Quite happy | 2.0 | 3 | 3.1375 | 3.0 | 6.2625 |
| 1198 | MA02 | L'Oriental | MA-02 L'Oriental | Quite happy | 2.0 | 3 | 3.1375 | 3.0 | 6.2625 |
| 1199 | MA02 | L'Oriental | MA-02 L'Oriental | Quite happy | 2.0 | 3 | 3.1375 | 7.0 | 6.2625 |
1200 rows × 9 columns
# grouping the values by ID_list
chromaplot_df.groupby('ID_list')[['regions_name','N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index()
| ID_list | regions_name | N_REGION_ISO | mean_FoH | mean_SFSH | count | |
|---|---|---|---|---|---|---|
| 0 | MA01 | Tanger-Tetouan-Al | MA-01 Tanger-Tetouan-Al Hoceima | 2.941667 | 6.508333 | 120 |
| 1 | MA02 | L'Oriental | MA-02 L'Oriental | 3.137500 | 6.262500 | 80 |
| 2 | MA03 | Fes-Meknes | MA-03 Fes-Meknes | 3.020000 | 6.766667 | 150 |
| 3 | MA04 | Rabat-Sale-Kenitra | MA-04 Rabat-Sale-Kenitra | 2.911765 | 6.141176 | 170 |
| 4 | MA05 | Beni | MA-05 Beni Mellal-Khenifra | 2.812500 | 5.975000 | 80 |
| 5 | MA06 | Casablanca-Settat | MA-06 Casablanca-Settat | 2.934615 | 6.080769 | 260 |
| 6 | MA07 | Marrakech-Safi | MA-07 Marrakech-Safi | 2.893333 | 6.033333 | 150 |
| 7 | MA08 | Draa-Tafilalet | MA-08 Draa-Tafilalet | 2.980000 | 6.340000 | 50 |
| 8 | MA09 | Souss-Massa | MA-09 Souss-Massa | 2.940000 | 6.110000 | 100 |
| 9 | MA10 | Guelmim-Oued | MA-10 Guelmim-Oued Noun | 2.900000 | 6.200000 | 10 |
| 10 | MA11 | Laayoune-Sakia | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 2.800000 | 6.850000 | 20 |
| 11 | MA12 | Dakhla-Oued | MA-12 Dakhla-Oued Ed-Dahab (EH) | 3.000000 | 5.400000 | 10 |
Mean Feeling of happiness (reversed) for every region¶
# creating choropleth plots
fig = px.choropleth_mapbox(
chromaplot_df,
geojson=morocco_data,
locations='ID_list', # Unique region IDs in your DataFrame
featureidkey="properties.id", # Matches IDs in GeoJSON
color='mean_FoH', # Column to visualize
hover_name='N_REGION_ISO', # Column to display region name
hover_data={
'N_REGION_ISO' : True
},
mapbox_style="carto-positron", # Base map style
zoom=4, # Zoom level
center={"lat": 31.7917, "lon": -7.0926}, # Center on Morocco
color_continuous_scale="rdbu", # Color scale for data visualization
title="Average Feeling of Happiness in Morocco", # Map title
range_color=[0, 4]
)
fig.update_layout(
margin={"r":0,"t":30,"l":0,"b":0} # Removes excess margins
)
fig.show()
# pio.write_html(fig, file='plot.html', auto_open=True) # Save and open the plot in a browser
# showing the rating of the regions based on the Mean Feeling of happiness (reversed) for every region for every region
chromaplot_df.groupby('ID_list')[['N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index().sort_values(by='mean_FoH', ascending=False)
| ID_list | N_REGION_ISO | mean_FoH | mean_SFSH | count | |
|---|---|---|---|---|---|
| 1 | MA02 | MA-02 L'Oriental | 3.137500 | 6.262500 | 80 |
| 2 | MA03 | MA-03 Fes-Meknes | 3.020000 | 6.766667 | 150 |
| 11 | MA12 | MA-12 Dakhla-Oued Ed-Dahab (EH) | 3.000000 | 5.400000 | 10 |
| 7 | MA08 | MA-08 Draa-Tafilalet | 2.980000 | 6.340000 | 50 |
| 0 | MA01 | MA-01 Tanger-Tetouan-Al Hoceima | 2.941667 | 6.508333 | 120 |
| 8 | MA09 | MA-09 Souss-Massa | 2.940000 | 6.110000 | 100 |
| 5 | MA06 | MA-06 Casablanca-Settat | 2.934615 | 6.080769 | 260 |
| 3 | MA04 | MA-04 Rabat-Sale-Kenitra | 2.911765 | 6.141176 | 170 |
| 9 | MA10 | MA-10 Guelmim-Oued Noun | 2.900000 | 6.200000 | 10 |
| 6 | MA07 | MA-07 Marrakech-Safi | 2.893333 | 6.033333 | 150 |
| 4 | MA05 | MA-05 Beni Mellal-Khenifra | 2.812500 | 5.975000 | 80 |
| 10 | MA11 | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 2.800000 | 6.850000 | 20 |
showing the rating of the regions based on mean of Satisfaction with financial situation of household for every region¶
#creating choropleth plots
fig = px.choropleth_mapbox(
chromaplot_df,
geojson=morocco_data,
locations='ID_list', # Unique region IDs in your DataFrame
featureidkey="properties.id", # Matches IDs in GeoJSON
color='mean_SFSH', # Column to visualize
hover_name='N_REGION_ISO', # Column to display region name
hover_data={
'N_REGION_ISO' : True,
'mean_FoH': True
},
mapbox_style="carto-positron", # Base map style
zoom=4, # Zoom level
center={"lat": 31.7917, "lon": -7.0926}, # Center on Morocco
color_continuous_scale="deep", # Color scale for data visualization
title="Average Satisfaction with financial situation of household in Morocco",# Map title
range_color=[0,10]
)
fig.update_layout(
margin={"r":0,"t":30,"l":0,"b":0} # Removes excess margins
)
fig.show()
#pio.write_html(fig, file='plot.html', auto_open=True) # Save and open the plot in a browser
# showing the rating of the regions based on the Mean of Satisfaction with financial situation of household for every region
chromaplot_df.groupby('ID_list')[['N_REGION_ISO','mean_FoH','mean_SFSH']].value_counts().reset_index().sort_values(by='mean_SFSH', ascending=False)
| ID_list | N_REGION_ISO | mean_FoH | mean_SFSH | count | |
|---|---|---|---|---|---|
| 10 | MA11 | MA-11 Laayoune-Sakia El Hamra (EH-partial) | 2.800000 | 6.850000 | 20 |
| 2 | MA03 | MA-03 Fes-Meknes | 3.020000 | 6.766667 | 150 |
| 0 | MA01 | MA-01 Tanger-Tetouan-Al Hoceima | 2.941667 | 6.508333 | 120 |
| 7 | MA08 | MA-08 Draa-Tafilalet | 2.980000 | 6.340000 | 50 |
| 1 | MA02 | MA-02 L'Oriental | 3.137500 | 6.262500 | 80 |
| 9 | MA10 | MA-10 Guelmim-Oued Noun | 2.900000 | 6.200000 | 10 |
| 3 | MA04 | MA-04 Rabat-Sale-Kenitra | 2.911765 | 6.141176 | 170 |
| 8 | MA09 | MA-09 Souss-Massa | 2.940000 | 6.110000 | 100 |
| 5 | MA06 | MA-06 Casablanca-Settat | 2.934615 | 6.080769 | 260 |
| 6 | MA07 | MA-07 Marrakech-Safi | 2.893333 | 6.033333 | 150 |
| 4 | MA05 | MA-05 Beni Mellal-Khenifra | 2.812500 | 5.975000 | 80 |
| 11 | MA12 | MA-12 Dakhla-Oued Ed-Dahab (EH) | 3.000000 | 5.400000 | 10 |
Regional Differences in the Relationship Between Financial Satisfaction and Happiness¶
Based on the choropleth map, we observe that the relationship between financial satisfaction and happiness varies across different regions of Morocco. In some areas, such as major cities like Casablanca and Rabat, we see a stronger connection, where higher financial satisfaction aligns with greater levels of happiness. This could be because these urban centers offer better job opportunities, improved infrastructure, and access to resources that support a higher standard of living.
On the other hand, regions like the Atlas Mountains or rural areas in the southern provinces show weaker or less consistent patterns. In these areas, despite financial satisfaction being lower, happiness levels do not always follow the same trend. Factors such as limited economic opportunities, cultural differences, or access to fewer resources might explain these inconsistencies.
These differences show that the connection between financial satisfaction and happiness can change depending on where people live. Understanding these regional factors can help us better understand why happiness and financial satisfaction are linked in some areas but not in others.